# Object detection

The **gst-ai-object-detection** application allows you to detect objects within images and videos. The use cases show the execution of [YOLOv5](https://github.com/ultralytics/yolov5), [YOLOv8](https://github.com/ultralytics/ultralytics), and [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/README.md) using the Qualcomm Neural Processing SDK runtime, YOLOv8 using Qualcomm AI Engine direct, and YOLOv5 and YOLOv8 using LiteRT.

The following figure shows the pipeline, which receives the input from a live camera feed, file, USB source, or an RTSP stream, preprocesses it, runs inferences on AI hardware. The results are either displayed on the screen, saved as an encoded MP4 file, or streamed over the RTSP server. For information about the plugins used in the pipeline flow, see [Pipeline flow](https://docs.qualcomm.com/doc/80-80022-50/topic/gst-ai-object-detection.html#section-p2w-33y-kbc).

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**Figure : gst-ai-object-detection pipeline**

## Input and output capabilities

The following table summarizes the input and output capabilities supported by the sample application:

| Config | Input | Input | Input | Input | Output | Output | Output |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Config | File src | RTSP | USB camera | MIPI camera | File | Display | RTSP |
| Config #1 | Yes | Yes | No | No | No | Yes | Yes |
| Config #2 | Yes | Yes | No | Yes | Yes | Yes | Yes |
|  |  |  |  |  |  |  |  |

## Sample model and label files

| Runtime | Model files | Label files |
| --- | --- | --- |
| Qualcomm Neural Processing SDK | *yolonas.dlc* | <ul class="simple"><br><li><dl class="simple"><br><dt><em>yolonas.json</em></dt><dd><ul><br><li><p><em>yolov8.json</em></p></li><br><li><p><em>yolox.json</em></p></li><br></ul><br></dd><br></dl><br></li><br></ul> |
| LiteRT | *yolov8\_det\_quantized.tflite*<br><br><br>*yolox\_quantized.tflite* | <ul class="simple"><br><li><dl class="simple"><br><dt><em>yolonas.json</em></dt><dd><ul><br><li><p><em>yolov8.json</em></p></li><br><li><p><em>yolox.json</em></p></li><br></ul><br></dd><br></dl><br></li><br></ul> |
| Qualcomm AI Engine direct | *yolov8\_det\_quantized.bin* | <ul class="simple"><br><li><dl class="simple"><br><dt><em>yolonas.json</em></dt><dd><ul><br><li><p><em>yolov8.json</em></p></li><br><li><p><em>yolox.json</em></p></li><br></ul><br></dd><br></dl><br></li><br></ul> |
|  |  |  |
|  |  |  |

## Run the application on the target device

The sample application uses the `/etc/configs/config_detection.json` file to read the input parameters.

To create your own config JSON file, use [config_detection.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-object-detection/config_detection.json?ref_type=heads) as a reference.

1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-80022-50/topic/download-model-and-label-files.html).
2. Update the config JSON file based on the model, input stream, and other properties. For more information, see [Config JSON field description](https://docs.qualcomm.com/doc/80-80022-50/topic/gst-ai-object-detection.html#section-qjx-hqq-32c).
3. Use the following format of the `/etc/configs/config_detection.json` file:

{
          "file-path": "<input video path>",
          "ml-framework": "<snpe, or tflite or qnn framework>",
          "yolo-model-type": "<yolov8 or yolonas or yolov5 or yolox>",
          "model": "<Model Path>",
          "labels": "<Label Path>",
          "threshold": "<Post-processing threshold, integer value from 1-100>",
          "runtime": "<dsp, cpu or gpu runtime>",
          "output-type": "waylandsink or filesink or rtspsink"
        }
        Copy to clipboard

    An example format file for Config #2 and Config #1 is as follows:

Tab Config #2
Tab Config #1

For example, run the application using input from the video file, LiteRT, YOLOx model, DSP runtime, and custom threshold value:

{
          "file-path": "/etc/media/video.mp4",
          "ml-framework": "tflite",
          "yolo-model-type": "yolox",
          "model": "/etc/models/yolox_quantized.tflite",
          "labels": "/etc/labels/yolox.json",
          "threshold": 40,
          "runtime": "dsp",
          "output-type": "waylandsink",
          "snpe-tensors": "<Model output tensor name>"
        }
        Copy to clipboard

For example, run the application using input from the video file, LiteRT, YOLOx model, CPU runtime, and custom threshold value:

{
          "file-path": "/etc/media/video.mp4",
          "ml-framework": "tflite",
          "yolo-model-type": "yolox",
          "model": "/etc/models/yolox_quantized.tflite",
          "labels": "/etc/labels/yolox.json",
          "threshold": 40,
          "runtime": "cpu",
          "output-type": "waylandsink",
          "snpe-tensors": "<Model output tensor name>"
        }
        Copy to clipboard

Note

Config #1 supports only LiteRT models and the CPU runtime.

Note

The `snpe-tensors` field applies only to the SNPE runtime. To retrieve the output tensor names for a DLC model, open the model in [Netron](https://netron.app/).

Note

When using DLC models from the AI Hub, the `snpe-tensors` field is optional.
4. Run the gst-ai-object-detection application:

gst-ai-object-detection --config-file=/etc/configs/config_detection.json
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Note

For USB camera input, set the `video-format`, `resolution`, and `framerate` parameters in the configuration file to match the capabilities of the camera. To check the camera capabilities, see [Configure USB camera](https://docs.qualcomm.com/bundle/publicresource/topics/80-80022-8/usb.html#configure-usb-camera).

Note

The `Yolo-NAS-Quantized.tflite` model can be run using the YOLOv8 value in the `yolo-model-type` field.
5. To display the available help options, run the following command in the SSH shell:

gst-ai-object-detection -h
        Copy to clipboard
6. To stop the use case, use **CTRL + C**.

## Expected output

![../../_images/gst-ai-object-detection.png](data:image/png;base64,UklGRk7wAABXRUJQVlA4IELwAACwmQmdASq2COEEPwF+tlOrP7+0pbHqu/AgCWc5lVrM6/nPrLYJ9VNXOIKqLjioPGg5xj58e95/pbmQ9M/yvgv/e+Iv6v/ovYE/YX07v+PwP/N/9/2BPCl5ydAv/2+mL+t/9vqHf9/fwYtlif/vE9hP+l/d9Srh/iE6S+st0Fm19O3oz2r/7f9oPfv+hvYM/Wr9kfc96fPND5n3/k9ePmcelh64XP3+1H+7uTJ/Cf/V//vUd8Z/2//5/tfN/4m/8/7n0/NkfwH/555/2X/k+4vYZ/9+df4x/4eTfw/ewFf/1E/N/ibf/e0v4n//9zqOq//3qG+/AnB56fqU3M4KLXxb6p3B7fYZYRbEjMVwIcEsFIsvoTx+BbJXtFxiN2G+YID6toCawdrJcqgDTP9+L8rODMIRV/lEYI3Yb5hHkgognzsMiWGuW3jBSx465beMFLHjrlt4wUseOuW3jBSx465bNhh4axgJ9V9dmmG+YR5IKIJ87DIlhrlt4wUseOuW3jBSx46lzlZqdZ7iZ3CA5QEDuFWGNeNKJJ9up40pBY5kPxMmjXLrto1fhDcC8SKzls75FErp5W6mzn1SIMj4aaVpWB/F9XlVn14Inpq6GifIEKUjXPn6xKX7mYNyGMIWoi4DF686QyR0XDZz6pUsxwuifOCDypZjhdE+cEHlSzHC6J84IPKlmOF0T5wQdX5QjXD1vPuL4WC7tJiCmxrU9XDIsF3aTEFPmOq0cWq4b51KPB/Okrp0KIRECrvaH9OMClglloslW0M24M1MjEKiYEFG1cWGq56TIiSQr/rVt3BNNfJHpWMZBOWLXHEJhvGZDCAYAp3C4SGHfcCemCw5acptNOCY/6/I1VG0IlWgjkvW9f3RpY1hJOiwfEv7o0sawknRYPiX90aWNYSTosHxL+6NLGsJJ0WD4l/dGljWEk6LB8S/uh/EaVvu0kHh3WW1RbIoNigxeUW+7SVkMkdFvu0lZDJHRb7tJWQyRwBrULF9iEH7DWmDj+pv2UBO2Xrqqn4aom9oHS9c0734ew93Hjot92kqYisoCyVpgK9s9hRHPcnUYdQq80YYWDtZLlUAaaXKoA00uVQBppcqgDTS5VAGmlyqANNLlUA15uqU1g7Wz3Q1udQ5uqU1g7WS5VAGmlxjiQv6GZhA7/IPot9hNaRsxOs7+e4dl78hsVpKyGSOi33aSshkjot92krIZI6Eg6ed9eij/ho5dltGWNUwU3tVqjoJdcB36BR94FqJyWBLwHlASH9WIFo5BrJ7iuCGSOiu90z9kJgZWzfdpKyGSOi33aSshkfpYF46vVytuVfl+VQcTKAm3+vlzcKnHRMomCJhIXWbMT5wQaoR1+kjniFD9oh6VVD54HXjNpPs1qfi33aSshkjot92krIZI6LfdpKyGSOi3k6+mPUnuIKMShisF5af613k7rouIp6rlyO5e4D1AXHXKnO7aG1zJDHEiGSOi3aau5rrYz4uDHSct+1+37Tv93chXirCpeVD5yLxmMGamnfTBocIbRc2Uf4uBGzEOrl4mg/PF2SoIJKyGSOi33aSq8xJ0SqmR29DiV6//35gkGHq3gdkAZeVpKyGSOi33aSshkjot92krIZI6LfdokKrQyM69rWt7GxneEILkNBuljF2nahGZZ5+DbCxnsAq+qIaD9EgXdpKxnCpyuZhXiwEQDThyy+A2Ocj5W6d1h4/+GeCQgNLjrG14lRJpb+xjnZHJ0riuu3sVpKyGSOi33XWzLYo16IOcKQCc3mR319ocws8kwyR0W+7SVkMkdFvu0lZDJHRb7tJWQqVY8AsrdFrIoNMaME1ov1GohK/BjZVKeEJxMfqLCS3B0wYQV9ab/LS3+v5DYrQbTvtdFvu0lZDJHRb7tJWQyR0LnpZkKdXqF4THsw5jg+K9hVeBP/c99yEc1GW+7SVkMkdCWfTNd3bVfGidQNAw+nkrW7HAvMyR0W+7SVkMkdFvu0lZDJHRb7tJWQx37IDlsD4liAVOnBMUwMAunCconJ/UtLh7ZdZZNfJHRXe6eskrIZI6LfdpKyGSOi33aKrztAxBHZCqKrqgwILswEUnSbFuTTGNEjoSFQepjKfACkNqVUVwQyR0WTFmIKuXcqhyfKJUKzwUjDDOPmi33aSshkjot92krIZI6LfdpKyGSOhIQOuWdDF3WQKNMfmWHPpBNbP0+6IA87SUPXcO2It25u1RXBDI+3PkGxWkrIZI6LfdpKyGSOi3XIs8Cyg2+hgsrcrXKpkwJo2qGgSfG71UIvYDER3WRUEEfqyR0W+7SVjl7wx275s9wxkasAXTU/fNspI8ouAktfJHRb7tJWQyR0W+7SVkMkdFvu0lV6hAbyc+pLQqJVX/8vclGtjlRkiEHk49xIO+9VSQUi33aTQ+0G077XRb7tJWQyR0W+7SVkMkbwcIiYRCCIIb4fxSoy59My7RSgPN/rCDrVOQuFDtE96mxgdhkQyR0W+7SVX/v05VTCxGclcehWL+6yvQUOqoJnlc+krIZI6LfdpKyGSOi33aSshkjot9nwgp2uP4WCBGqtgmUo9VXMijDlz9jZ3pLtckAIOFWe13aSsh2/aDad9rot92krIZI6LfdpKyGSOgsJcfRC7nRfndTXyRJ9nSuKcYWYnCA9qPfJw4CvM89KqiuCGSOhLPpmr4DXtjXwc0FvcrSzP06Sr5j0W+7SVkMkdFvu0lZDJHRb7tJWQx36jNGxx36IhyRkJK7JPORKqiuOLLet1SDorghkjot92krIZI6LfdpIAY3gBJ2XZA+Hvy1bimwONzzWBHAV3LL5DeGdHAr+I9oOfBieGjDkV78hsVpKyFUhqaqXwTTvphldskpbpLXNXoBHyiqK4IZI6LfdpKyGSOi33aSshkjot5OvR/T1kW/+gOM0eeoWKiDDZUCpCCo33aSshkjorvdPWSVkMkdFvu0lZDJHRb7tJPGdTelG1ojaUiqkkGuwJhwbJBnOtkDOd44S5ng6ChlzC2NBRkQ1yi33aSscveFEDFKAe/QXdDJBsFYZTOYK7d1qVUVwQyR0W+7SVkMkdFvu0lZDJHAF/MF/Tr7RUDbLfdpKyGSOgHyGXThkjot92krIZI6LfdpKyEgLaq0XYv4B4stjU9D6MkALYrWxg5OPpBKQyEPGhqHtlGo7v6d2krIZI6LJizD6T31mauy2H8Wn6Q6etkHPbUHC/x0W+7SVkMkdFvu0lZDJHRb7tJWOTdgafrFr0rPhy2kH+BP/aK0z8dFvu0lZDI+3PkGxWkrIZI6LfdpKyGSOi33VSPTe/b3+TgUZNnJW4M+X3J5UVdjJwOCIiqrA1aj6pwo3G2XZK0F/HnjIFxs2i33aSshkjgSSTlTHDtytvbIJpHOt6rvX3bf9dFvu0lZDJHRb7tJWQyR0W+7SVkKmGU5rPtTn7JHRb7tJWQlzHhbLfdpKyGSOi33aSshkjot2Jmf90Lqdj4iVi7ToF9r9x9265siEGps+AiysPL+Q2K0lZDJB5Tz1/eJ5eatUw0UUoYxge5DCoqd2krIZI6LfdpKyGSOi33aSshkjgCvEOXVz3aaYQwuyXaS4IdzYrSVkMkdFu01d8bJHRb7tJWQyR0W+7SVkMj/ADcgEP7oEHG0LesCL5AA5aOq4Y7fx94RG8SeOxitfab8zEf4mDA2R5Rb7tJWQqTsRU+Sh5iYpxUbUn5s118G1c1ijXyR0W+7SVkMkdFvu0lZDJHRZHcT2aFFWYL8XqSO5Y3bcSQyVd/qmLk6qCGSOi33aDad7DSVkMkdFvu0lZDJHRb7tJBvguX8pNxPN1XET9ou32mljLi3HXqXMcy/mFoLa2DAMlY6zGeqqDp4uwu7SVkMkdCWfTMHKHWbFW44Fq7eZcTRXBDJHRb7tJWQyR0W+7SVkMkdFvJ1qAPlgxXgNY7PXvBPrT5zRCDlzwVfZZeJScFKbwAONyi33aSqDiGOVkMkdFvu0lZDJHRb7tJWNiyU65CYYuJyVJKpeVIgN/oaHsyi7qkkx0VHHgDw05Wc5EepORQ/WAlZrTCScAk/p3Rb7tJWQyRwJJJb3MzhWGSCII8CQ4c7z24YCSpioCbsZu+V/pKyGSOi33aSshkjot92krIY79jLt4y9y5MQD7b/rkPhthvWyU2kxJxZNfJHRbtNXcoXdpKyGSOi33aSshkjot9iKVsIzADosnRYy9ABbf+ZBhy9gzaoIAByeJ3cqKKPLO9mFyOi33aSshVIakNQOuXyfqLolXkTZYG92IOeCPpzrKCHQUm2skRNitJWQyR0W+7SVkMkdFvu0lV6ez+YGIaAnVc6tbxOgPMzRpgsBWomxWkrIS5jwtlvu0lZDJHRb7tJWQyPaMz4wdrHJ1nQwETYNTrCZxJpaDi6vVEUO5J+K2uAl9SS+Zm06DiEq7b05KIu9/KAYQl2Mf0/10B+3EqqK4IZIQjLo+cSYRophZIm0A1In/PaLZZaHAoU0ANUX4dt3NzOdiAk0PtJWQyR0W+7SVkMkdFvu0lZCpZO72Ty5XcxbfXfBtKwRTgHX/APO0OvM4n6SshkjbGgnRKqiuCGSOi33aSshkjZ/WWAHYWPmS00wXVyrnX4vG3iuqAF+4/xUIW19PGXZSM0c9mIE4cC4vIndIJ54AN2V16ifhhZdR5IssdDB28aSYIgTypy1q/GcghwExpDrVh07tJWQx4oGYX+CvNzyhsNZjWcSgeDskZrYRmJQW2npVUVwQyR0W+7SVkMkdFvu0lZDJB7BriOkirm8fYyLDJ64/9j+wkLov00So1kG33hctt0u1HqByi318vXmu7SVkMkdFvu0lZDJHPxMmV4NUEEJtxOi7ZQb3hh+irI6XIzozFqn+Morlw8fgdIqJfORmnvmK0GO1SLa2Sjf8QTG8oc27nfTBXY3rIYxSZvfIBzb8TH4Fl1inb18e+JsI84iMNZj00skF3xznwB0fnGkAvBW6YlZgHxgwQoLbDKEFtQ01l/5eQAMUlL+Irti1VK4tiZ7So+wiZNv2mC8W7fPM0UuFoGDiBeyJRij8mTDovV4vsDmCUY3cot92krIZI6LfdpKyGSOi33aSq9Qf8AcNP8O2G75UqkzVKoFe16Z1tsP3YcnjVmDAi4sVh3wwayEuY75+yR0W+7SVkMkdFvu0jr8zqghYHWtlamSoHa1/R6VQ1xd2bgJA81ryMXpqs29ymkIiiN0YBbIqABMFIIGCLK0Sm/wPaxuMx/pV9UyNBrkBCV51mYW4axO22CFXgKpZMztis3uSTK4LLBuKRf3PaonhWdtTsOhDT6a1hG+s00IRmrxo2L1geKaVSQlqr3aCYibhS67uDxkqrmrfOg53m1uHzKEsrch87oyrfasGmThkjot92krIZI6LfdpKyGSOi32fQw0H9Z4RX49KanGS3FuBrbGs59RuH2LZxHNWCQ0P6PUD8cQmtOXazT7mmHmu9074wu7SVkMkdFvu0lZDI+CZj4ELUI6eAm1B/kkM5yipX/2Wksm/JE4ZjkxSE/ZnbMMLlq8qu/1gxQUSkaDFFZ2KnwMWfYmHwr4z0mZoEQmkqH3vPi7F00S32ng7hQWcBl5mXtInWPZ2Sv0FRbL7m89AW2iHvFjPoKafuP9N1G1kLCsO6OHgQhtdsflzYU6z/IQSsv0kPuheIlqxjLknVq5WLq2lr4/pMLlZlAGvzLdG6r5KP8RVMI+iLCpQ73laq28mwVrWE79LNSsnzDPX+LAObqClRBCElZDJHRb7tJWQyR0W+7SVkMkdFkgRuuJzH6tbPGArKXIhf0cFAYbYwjlR2oBzOXnylm/xxwh8q6o9hqC2VWLghkjot92krIZI6LfdWKgi4WbPWBtY8Ng9SMKqzaDNT9lJij6FYzgUupi3SYN7wNXcoSAZztBv4Ppya275Erjtsa0hG7jSz6RcVbXBevzG+mrPAl+Kd5ni1HDi8mbjqXK7L9g5Gxw8mYajkjxjLrVoZzkuvXM8KgO0Br9m+aUliSNTA7km8wVBKxouE33fotwe8frV1/Ou1eN0RQ3eReYFm9SlFBafQIzD7SVkMkdFvu0lZDJHRb7tJWQyRwCmx0Nd7EqRWhh2Pj7UNoHJKcSlxMF6Fpa/+ZoGppcXIPE2TqIU4NdErghkjot92krIZI2wyI9g/telAAGFD35Xs7g7L8lfaP758vioz9R0SiG15VYtMzHQN/vcXU4DX1uRlY5ojaX48XMbWf4wplaV+LE78Na5nyYMoeXNoB/IzSh+DdK01PfOuK/jd73P1mPtFYN4vD7zDjJULzlP37xn6WEaYsgG2cksWmjxd9ziV2W4FWbV8zcCzH7P6pF5Gd/bBCD64rD/agouW/7KWjpn7L3yjmXqub1gwbq18kdFvu0lZDJHRb7tJWQyR0W+66RzIN082NdZ4ywffsXoOPPQg7MR0umXyDWtrXmPC2XzB4NXitJWQyR0W+7RVBIN0QM1FpIszKhz8dCsAR1fRmjrqCY4B+HDvtxEyEs9TQeQYjnq4wgjf6k7vEQnTy+P4vIkU7EnKY/g6Qea6XImAmh8NIiAbmwgAu93xa51sKdoZnhLzUwlerbPdciUcRWZ9OZJWwtvjHxM+EUGH5k95H86R1nu4mM2t5uL70X9hkumTbc5jhQC/BVfxwqC1Q6LfdpKyGSOi33aSshkjot92krHJ+XheZ5td8WUPH8N+jrGvTzDLTHHhTYWG58WWm5Rb7tJWQyR0W+7SOzzcxYIM84qttP+EOI6dqLYkWTQweU/Q2/m/Z3ztPRbgHN3NtsY5/xpvkE+YZ3tpeVTfIhE7gHFzbe32Eku6Pu8fHncQWDWVTQur0023/uMCDYFLSCBJL4gyyvYtzIQjqXWGuqCPM2qmoK4dNl1arpGuYkzmobFj86crpgjSszjYFf2j8IOH8eIP9gV9TEhd3mj3NXpqNdxBw5BVDuKNfJHRb7tJWQyR0W+1kkrIZJKgxfEVYJkLm4sGvplFCZLnDfmKuUf3J7IUL7hjnTVG+5muFTamvkjot92krIZI6LfXCui8eGUiE85o2XAvZwRDeBF8bfDW+7dNbjWJQ6v+1wzQxUpxgmthZTZfIQhvk8QFJEnOrXhAbVho+z3JjZ37uxbsQsjDkGNRQpKklDk1RelXDG4sCOsW9o2LvWXP9HmN+05au6SpGcrWcyCo7/57SyKEw3uDi/myYn7z+XLJTuN42l5F23mJ3YkG6LhjwVAKvw1Qt3HRAs8sp3K6zOXvfm9rkxDdGThidw5Jiya+SOi33aSshkjot92k6ZtSOSZfsrloiZPW3kegcwuVeDoKmf9v9EJR+lypRDVyW0HmF0OP583yF40W+7SVkMkdFvu0mB868kCo04CrxAaO94W33h0LqDuPpdTZid8UUoqpJbB92hdLm8U4mu4jDvrV5cWJYJAKfVCUKLXbrmmHjZdZ9tz/T8pA8XuBA1Q9v4fq5mi4g3lb3KKZg7YgQBJK1DnmiWEUJQpJkNRHiK/5Sgya4aaLW1nwKvojT/OV0+lG24U3q9743PnDDeqTFGEDe25PibAu9iKTAyY2S3yRbvtmzzsSVe//4gtfToD8HXeLgEReup6vCsmFEVFGYhpKyGSOi33aSshkjot924GKGPNNRvD5dQZ6iau/1tpec5C2yzj1cbhPYaMszEpT5R+6spv1iJbC5zMkdFvu0lZDJHRX45g5dsLhs4KTtfGAj9kCiT+abkvSvEb93/l6mBr91ycS/eHuEwBlBEw/okCiBdxeBurQtcQP0xaYKyLlLwUUuCc5AEJmJsT9EsdHejRjk2+aY5flQ5UpCJGM9ZJr7M0BWUa8uqOmqNSuSMtCOdkwWs2moQUE+yrujUT+CYA9bim1pN63X9D/XtkCEE9dAXqPx/YjCkwO4JCxE9orw88XpX+Bs72J1qOujUNdxfTDhOBbNwBnyUnnzFbb0SDmYG1ZVGnVAlf5KNbE0rtyuCGSOi33aSshkjot93b0fSO1cF782oMG6Mx/HOS151Q448SSkhUaG6CAZ5L/UycY3G0oJLKeTHNGgyYXOZkjot92krIZI6B6m7Jrpb9nw0QzzvkHsHVxz30BK/AU6mJCOyrGgyjj+xjS6cpcCCr1bXTOu/N5dtNPyAsXQkWlFI/qtUQD/wnxuBg38004Z+v3cJAZdSC+qoAxnyIiz8xn/5Hm3OYkC9BWsYaoBgiDCOkm/v0O2U4iPYSOulHrn1xYvFvk2wWCmfr/bCIq3FX/zKRLZzjQfk+Djh0MZgEaxZeHkVz64MK3iNxuxDdiC83kjBRahRjnHjbNi3qxWUWP4zHWQyR0W+7SVkMkdFvu0lYFGPKpbzXPQInPD6K6FcJrE6bhugx1H6zoIj26PuQHWKfZbY1jVwLnsTu+0U0mS3mKAoQUDUVwQyR0W+7SVOjztwtIn83BbPj0QD9qkbo3Oc2z72QNfH043eZNtDvXgo8Tf14TPRlyaFw0j0CttRHpDF2zuP2G8e3IyI9qGFNE8xR8GdxNgA5suk27dkQlZyi13Cd4SWfU1OZ16baIYcf80nnSagzk0cSR0zFnDsCHd2ruZWrmZ2A5cFlZ9ACfKLR0kAwBvluTJW6RWtMzXrhpYO9NzxPIknlYOaU87lZ6XEfv5ePlsuCOFLWFID8k29B4Ja32JSuCGSOi33aSshkjot92lk0oM6/gYTCfgyK+gjQMneE1vjOgnCZOC2FJKko2licmYmk75tBd/XYMeliIliQDalCAJblgCd5zHWzlT98ulWwxn/0+iFaSshkjot9kXOfSEOwMxriOfhTpEtay4eHAtECWYjoxTrqHTp1lIzTSooO8eEX4plxAKcKkuVEiZobhQ3dXw4A6TcPk2wccbYQYGXS68NM8cmiVuexFCd+gpfRJRoBCqDmRXKFte5ohcnP5Hvx9Re3Iu+IAyrQHgk69nuDt85BMuW5p39xkYrS1xVOMqM4feqKGCIdTljE6bvMCIaH3WforyauOIFYaooxZujNP4iS+pdHrJ/CraSakBv4UspNuYIdXbeybIZI6LfdpKyGSOi33aSUZeh5qVfrWOTukBT1yl8XADaJMohmNuHxVJfp67EQwhcLkVj469Oq4gg63Jgy0epRkR1yf7iVFrVA1/W+xcKe4G+ura8fad/7KY7kEcKc3VY6LfdpKyGSNnMEsvZElgmoTsMyi+uD7dvUJy5pAFOfS5/U8ztikjtI3zpSj0ZiRDGTW/roNU0uMAyt9kb6V6Wu8Q2r+Law34p9a/NCmjIJHsxSAZPxOmsb+yBbIDQYfenGbTUkTAhZ0F25Pcb4jh5sWpVaCLQTX7omhMBiUeECVx23h9vJBFLBjtQoN1LmGEdiyqFGv+AGWWeL7gjDes84L/OHIgtoOxECtUpDpRi1vDpYVA6e2FOx9/edcq5aZGvkjot92krIZI6LfdpcJht1tdOQwdHpuTIQElW5YZpmHpPPP08I/YJ7Blk3WSU8z+RQXE4kI0S/41p1tICHaDMLuCVcx1yL11TKkEG3oYyQ4lrOqhwjyH84hjlcqZSqdlSNsBd9mkpAKKOO5o3TVMqq5WTVWjHNJKBPCxgopp13KGOcI1zahZQFBAt4lgaG+KW936alzmEUwNlDiVe2oz2qIvxmlL0PjP49P69xVvL0N3HT0ncF2tarMVdjROPuyVo3uB5gDcQtUu0TDbN3eeQgJXWQa6N57p9o8Nd6Nw2wSb6UmAXfw/hxZIAl4WAShUWIIGuCJ0nNiM9kfj+ce7aMLOXJBwhi22dBRT8emFYx2rltWsfkAP8gQOJqcn96Oyr3B861P+vLDoaHOlJX0aOi33aSshkjot6KXgQwRPIB2ZDDx15RmAax6MIR5RfrvmewlmsSxQUHEOu3vIEph2utDoScvVA/dI2L18pKX0BEOIccRvkaRdQ/Hxa5QkQSxNDL4TyGr6eO5FMd/7AjYNhc6zvdwsCJ5uDlcUVlShy35dcAOcngJVwL0Cw7RiiqCi99DXFp6nAcEKlvvJF8IJdJghnnvW5VmB2cbcQFGCloQCZxfBhcjoqEm9CnNcLhjBPON4r65OGx2uGQAlyFMMN2QesYN30+OWcDdz+YceWfIooa1gNNKKfUPA9RbPp595767r1Pr+nkJ9z/vBNzmHc1XdH5uDyQJ7FcX71BGfz5s8FduWvQHGswfkw8JQiL+C02K0lZDJHRb7XJbhPXJEht1Oy1oBtijzDkBX8/vFD2Ip3fdG6fXGtR16Ow72S+l5oCb2jqTuNv/pn+5cCsBXDDXIpRJu4f1Y54rVSWVMSbjccbRE5MuM5/n9tNjKfk/dr+RHFKlOXt28WDImHQjLdOT0AOH/ewYAkJgFWIJdvHBBoC9fL43JnOttxoXvXY5w4mPlyvCo2Mwkyq3W0WQNWcC/sXax5kGqS4rmxyYDkKpCWRzoh/+YgdkQ/gyJPhKdEIJHyP33GfJ41ZQyzRlPt6Mzb4hfdM7EfWGRe1oSYpGUsMkeJpRxwgRbXf6vh1DUW8Hf+s+BPYxWtvBbD1DMgNE03gVNr1Uft+XGCJVMNHHTqDQrcxvLqPOIXnVDCwSoxMPcrjN+kBWo1NLlUAaaXKoA0zcmjRUISTAXkUo3EjpwHJDKkmVmk1iD4d1IOX56mg5dLZAF2dU6SCEXVDyWsr/7qwFMwfW6GLfJY+TW34mPjqiWDlfsAKQc2LiBp3TpagAJ4YgBiLqH0o5f3qmWyTQ23yL1dE0S/G9YoD86HdACEUgCqDV9ZRZhZ/4Yc6A5J2s+ETWVjs8o2jM7N/ljrULFuIBSi+TlikgRpnhhIUK4RRm3YgZSl73UxhKlATYlv45iFllu/SGss0/oDEF9kSE58TU5NLSD+wnMxvq/cgDlnGbatZPQVBVg4VoPFgZ+vjmyPgKe2uWISDSqf6S3K27/xxEtfb/PukKMXz7+CWtKqJKm06hMuyBRAbzqhg0JcwmkGBoR0SGkb6G9/CaxM7QNAvCQyP2ppcQJIFXgFDA00uVQBpnnUbvoY8fcdHsxJElI8/4f41/wKsnQ+IZHX39wZgICSkQdyBElQDqR7+vc+WDtaXxGFNmd2m9ij52FKmtkzUcNxXoOymQwVfUW/9u300gQo/km5SvOZs8ryaQE+IG7bJ/CD7krZvvmb/HpiPaaKtn4TBn1N6SO+phaV0wlvvptqLQ7tys1GmlygerfYAX2311pCkbWvR0kr86uW4ctguYyrooGN5VmxDXZNBxx9qkw1B/rOqIOi8FRjxh+WNl9oSyr+GyweBtFmJmwH2up36Ph56C1SVoHVKvE/f/EdVTRgPlaNjf/N6M3P2VjxKfk+pnDYk4Lpv2Xr96vuPZ2DTmvjW04ZYp76fQIg5dM/zu/ygLfZw6ZySWsumiRmVKnVCoaN/vLUYizk+eVkQ0FEKnb+d4ygHGkSeEdxiFH3E+1IwXD7QzMNy2NwziSSbzyXNLGsEwxdS5gOF30jWzviMhBCJXtPhfr8qf5rt2iAITrxrHJGK9lvkAQbgPfHxIgRrJZ3TQB0AbPJ4K5i/ULFZbt1c4BGY8Wh3a5Zj4GhON3D0gEqO2PSc9J/BGuBhWM/iKyaRb7OMlRMORzcKpIHi7jLfWXY1AJWNrXJSssz8cCgToWjcQMFPB0IU7sg+yUuYxRXR7kUdFsXobsSIFQYg3N7FAYt8lkQV+8LMU9MehBa6tlWvJdD8Hvy8XSUSGTbbP69RZvTARyhUJva6RpgG99x9t+XZa3gYWCeKQMPtFSMz+zWCtwb63BK//Y5XiBiQBwvpEIb5GMhcT3fO4D9IgLqSX9Up/lmU9008410SCQXyCfWn0M7fHXvytlFtfLqHp0VwDYaLlwjlcdxmDWRBGgKTRXKGMgVoXUIxrtjXSQXORrEvHclJa0H6lp9YjugrIH7HRuUERvhxwzsK4biu56iwapdYmUuOvSGHWILWa5AFTi2i8hyMKCoi9ms4Qe9Opc3Hq+Bt59j60FKQVr3FUUPH96+EFw5GsQXD6JzPcJaV9EW1+CGCuEG4I9iIJDla3RP0QjWAa0c7vsahU4aBt9g05tJyjEI8m5eAheOt0C0EJY7LfBUQGbqE8zidt2SPKENEf+7JAzmhCfLk/v0eWRsehvHI8UEjlyddaHLbY1XeExWMCLdDp/V4Ibr3v5SiYq/5YxKrSTU6z9pOdXKUoxnlhue0kuL9Bz+sfy8WV9b79zn2C24ZNA56xcBuRXcZyMiPN0ZIe9Oky3GZxohmjF8fjk42ptqsu3PlBTrWtA452gKWXkf4vlg1/Vxit24terRDbmUQdSouzn/l37jy92Q6PaN6+fiiLpB2yu/LT/e8NIB1SjaShQ2ryK3z25ygXTLZbB1U4j+bk2nehlnlx1yKEvCDS2KPM5U4GzB1C8JrAKSwEdNMl01Mtc3dda5mnKJcjby5nF6yzrNoGVSiss61cc+xqYD5V8XtJhE5lbM44WeC/NEACtQ7qEQ+l+FQIQO1HCbnXK/5OsCIr8xQm/eSkb4ZjM/v//L6e8il0Me29GzMQjnbFkC1LWyYVf1IZIPTqHOWasMvKP82ecKw3thZlTvdKc/4f5uAaa202bUuX2xbsvwnApvZjGn3oAoiCa3ZJ1n1n4BVkX8YuEUYMh1WtNiGXICI+5g112jxFZId9j9GGCAkfYV9ZnE+TnSx5q4a3KkfkEgAajQGOFGImCCBMpKYoM8k0dppvgJfRaP7LDgtUuj4cpt9VoCPvMm2nqI8WJ4JU571IpYdEDLibsCYj2H4Ey4Um/4BJCfdocGyWED+e1j2E+RwmL30LlF0rsQJb7voIsEukeWq85Ox6KHnx3NQiJAb4wz5l5itck4tcsa026ohdAMVrsMEwpcqTktCapoKSO6Fmrexi4z4jUjnnJqi48ibQudIUusghvzjp7eu+HEsnoik+H2x1K0sNjOIhkDjGMjoqID1htQ+xBK78UyJYn98tdCCs9pxjxj6K3cI2ZEHbGx1yfvfwIg/pP/e/d4CfxYXZbDzGV6y2J0BScufm4H7tyZYhOc2e5Mkw+RjEBNERBP96K1UwGTiNXfIVlggBfZggd7OlfXuCpclbMJDPjsr22cO9iBkfOqGCD5u5Cb41K8v7fhsntrD3DI3XoaPL0mxlA5aFPF3g3aivcoRsIe0GBFayMHn5S99doQ195Qj33tPg1wfeNVmQy2469iYq6e/PgVau/ctibdaLjogdNAQ0plNA8Du6kzWbGkRVkA0zo57++jc3wSfD+/nvsjdtSACmxtWXhnmtrYPmXWbcFqJR1xCstGFc5N7TjijUbCsDmsOoQcN0vZUXRPzMgP3ZMxNJcx+YkoOMUIbhhXILBEzu9tPwS53HXOEKnH8zSPaSL1d2hpNHTB6ErSrFEeaMRd+DFOZ9pf2tOGDzFJkGGRtD25LLDyExzxYThs05djy6au8KErAiJtmSEj+n0FhmUDM+yEAGGVuFy1SocE+PSxSui42UNN04coxKB/abYv7ewsSeAvertVQ0EQaacU/MghHXWtyw/gMlA31qQqUycw0WH2qmQi8lcUgRoOHxip3VmlYS+JBDkH1Anh01tykY1nQ42RLdEV4Klb5Yrofq0xPt0MNrGdKnbrSuYz1TIguOvoicZ6RkKIVl8gKy1rDMNb/oWoqffj3G3yL1jcOM+tw2p6FzIbETN98gY/hclfwitTdvWpbhsw55D93H3la7CiY4vDL53CeW+YZmKaKdupmxrFUWOty1qVLcOUmLnNmXycmfTvmuv7X52/+eFVfjjxDP7GBBa1AB/o4fYdKRnnsv7EikzPrwsJbvz2xIL4Gg09eu4oERk9JcZIshJeY3jbQ7S4v/+tfOqfvWdAbXlfVTjIE78+8iXXR0oyQyYky96n9O5EWaaeGF7eOboH/gdvCX1dSP8U+tub1hhCBKobUlO2qEfAZ1avkp7nXNyYSQKPNWZSqCJcICsyk/Xpreo+f+PS8/zEJKjbao8yBkKW/iwsE1VJCclkk5Fmpoy28T9SseD49GnXwKIow+k32IiD09U/Qnfb534wCKuP4UyvOZ8DrlF7/gw8LH5FMqcHSEvxW2tLT8XtqZf0Fa9wPu7rfVK7MS2J8ieREiHMnH3Zy8tQNd2IFHTRom8olprzSZ3LRXNDZybwwVliBOims+15G2VZJWuACfbQSpTh0eTGrbeX2Y3jMwt8bdckDGjUgBeOjsquYT6fdUxbA1LFUZd1zPZhHT9JjnwEYkA9xkNfNgxuH8ypDb1T1vowKW6t7dgZIReoORFGMWQHGGWlab+tS1xoUEd8VaBBHSwRd0bGHcAe325lFv5tE1cl7VgaXFT0K3UXfS5e5rmiTh/KVgzpbcBR4kETxjDei3pVxBu5euy1M7cy6dUFRsU5gaItE2b4cln9T8LmWxV/8yTuluVJ6xmT32sOboI4wD8D/mM6EW8kux8NKJYYgueT4lX/RIab/HnEikir7ZJ4cuCjMUH3otyB1Fcm/T6hMnpYKb6/0lGbuv28LeuK75myOxYds5za9WHwiN48zc1h2E4i2YNhM5Lxv3BTIy3J23LW1T5JDm0hi6fikHEpRhl5wcRCm+1dHjNYPBzRqk0Sqff3YhIVrhxokB8ON+X+T7CvmL0c1Pg0E85KJ+LdvpK/11zn4Y0OKGT5ycniIvO3cX/dsFSBZ+jBB1kIy6+9gWhZvcsB0MtUu6FvrFXAdVLKRZpdrefM7j3bn4HgZPPXg4t3uugHP3MzEjcTtgRJsXwa+rMRTP2pVC8S9NvfccspsfLIbsjbHe/SFQP7Q5isMGcgErGnliKselhDQlHZNMlbFbuZQ5ziPCeGkD3VATmnjtip+4CPbWJDcoPWICeLt0ZMT0DJ/5XP96IwgIAhQ2xgtjBy3qE44izzhBkHYYYewTf+LU+66icrPem5g4qfOpwP+bQ7x5A0PlxMNp+dcCIgIY2yQtwO2IR3EnwUxJ0/wVBYD68NruvP1Gwf0JRteUYcNsnwd1RxumDRylp+wS5wT+Nb0OqXRY67o8U+Afh79wYL5zDT668gYzf7l9brtYlLB6Ry+rNYEECliiT9GKfxs0uhiZ7fOidjmuYTOKQvZa2RTf6KdjNJIBs6Kv9mcW/nafmkiwr3PrxCHYyOTVwmYe8Ik68vSCIuTCBqZn4R267Rm8afKoTBeX5I2+d2qRpkQgvVZGYhkaJJgTat62kfwOtXbX+JuChmgSIwitpa3sXKkd1edFfkutxZDNU6SUK6mq4fGHS3qy5txExzaz++aLepiP/bW2q5XazUayK+xKR8nKyVo9HieoE4d7WQwFyAJ2rp5FzHWa2hP+QGZI0TNGkHdREXP6pG5i+ulgGQnommmnUBf2xyoLxLaLhrb6OXONeYgW6NwHSDrahamsClOpQVAQybyukwTL1XMbU78QqPLX4M7UVUjpfMB39H+FKkvpAd1vA5v8edK2NwW04NEmBYLqqW/ybkS+t1KX2dZagzmb5t6OeXJD3wndMrXOdXnxE2w7XLCyGqMXQNEhgvu/lPb3B91rh8rQkGVpKNnkatTy9rvpoH1FUoBxYlxHylsENovJcojFJ9UiV520ofdawULBBtTwikcYg/rRnJpNE26wTTyFpqmr0JPpw3VnspjiCkm0EBjwyUKMeSFQTR4Emq3dj9V5JPZkKO1SoZ8Kx/x4F5SAKsnvNFFZo6LcbULD9sEfs/6AbK93bAIlG+Yxqf1d7g9fR/YVe1H1R0M//SOie0A95ZJrSvwD/a5qfTLQkRPjyWV1+WM4ddrqkz0IREFFSK4McVbLl7JaNDwcueAYiJrHr9VXOKp4JicArbp5hPribkqTJQSwrmypU5JXDv7TPh/JN3UDrhIilr1BlMHfotYFCWceIqvooOFC/7vUio42qazMltMU+sMapGWJx3//fWQxlXTrzS1uF8cW5WrfQVAyZt937GFPlFxZXFvb6Q0VYZBxd1GoC3Md54N5nWQ0BCL5Am2/9cz4c/zYqX+BGJ5Xa3zt7fQv7+25msgmA81C/heCb7jsFQlR/jbW6olmHegKveg3JIjp7mi9CjT0dltInccPzjqcR9c3vZCrvAqnhnubQhNGpGlz3j0tienG6jiDNFPwPNclmVWiVN2UIkI4dfp4bOBmLxrD9Zlcdlvuws7Kz3w7rT24+VZ++6UQyyT2kwbp0sGLR9zhgYdG8pbKoiRb1ozDZzoltYlqpSjNqoqcuJ9OQyqfLExLt1mDrYl97Hi0pLYICd9ljzyV1U+/G9BJyAl19DJRGTypTyiXstRTJSo72MgVepqDJThQeu7Z4z8lWG72DVpGLomQWuRHh8ZbXZ3HLF2Xw61GZqx0GwmCyIgVnUPUcmW1i/mRJy+kRqHMLwizjzsBO5SjY7Fb7ei72jCvY1P68/ReNUCVXVhiUlE3MYZc0t3jGgW1ghO/nV3N/1B5QMx1C2tgmrJCLhW4HD6N+FgghqFn+JSci22d65kIUs8pCVK4RtD6+ku47XmSYjHQeUALA1uNyZgUqXjgw202wKgIKS6BbDvrqgiChTdM2+ugjhD+0TkiIgjqTYTrjmyxPbDiXkTjOcU3gRJuHqT2EfF24vkcH04vefklZs2HY0rtbQc4SKCUgHwBe4rjQJK/zGrMeZsOLx791ICBYKD50LNkB0VPM/sicwY/wytnk6hhrY+O961n+aoI9VPpyO3GzSyw/EReKkk+D/9Edjdo6lTJlsOAcKPjKVDaCswFfTrVe/WYXRhNMHrKQzjgj9I4H6WnnKBELkgsxdDtqmYhh87PnWIGrREgdKuhK0bmUcV0vEnwvry2hSuIjhfSEkOqe2bOaosnFgzNaEJfMlK9pOpFtQius+cbT4qmo+yGmkxsf+Mdg4+vq9V8pe5ZvFhQQiSuKFkxkCxgHEb/TzktfIkzS6CcG2kurjmDSLhHAYmNcCPo7cs7Utf7+s+yn1Sn0vqAyOutxgVuUq4OhpPpLqkZOu2N0mF/wohHrblfA+/H13N6GI7ipE2Hnpk+y3oRWyi2WYnWIGqTUa+YRsG7oep92uDek9WCEelcIoSrR8bTbEfI1ROG/4FKyn8n/ngxxgriYG5HAEjoDhurHeL/bYuvsbLwOBgSuD1uuvgtTl31DMwMUhLiJLwDiPE/dcO3yeMzalbakkIVE78OEdNq5i1RToeQI8AbV1bXO1Mdv2zgTSq3h9LYLS7CgbedfUM0vE42p8RH+9c7NNTx2Rq0Ny3VnHaSXNXeUmUR+kBcmcJ/Yfuo98lG9m6MgPjSGe1QMWevu+DHPzX74MgAevfFB1AaJl7E266JJasEC5+z2BHWEZlqHhP56tZBlhuHsgu7SLavNrqA88OO9+Lnxv+1gKoo2RbMoCyWhcvTj+tWqvCYW5l1ezutSEFop+Oczzs/9Bnth0Ht9kMStbdAn7VNpx4vTKtHI3MVQjKevVgeevM9eqJbk9cB7DSAJvpvIVGDwYD4fnU9TuXsI8tDm96rUm2/xv3Enqk84A4d/S0zf48KcvZMOFfPc+CJPneACjriPLM8GJw2fAkP3SRF6Jb3Ome3u4Mk6GG2h0wH3iqQpseg1VTYSxCg6jkExU/su4W/WYaiY4IB5WI8ZBKF0mfB39kv2QpobPHi7CDgYxmgk7Y1cFAB4XVx2JggMEON48KUpwrPKjnUnUNi8cfROua/QcLhopDTlu4QdvE0UNB/Db2EkJeEGvd0ctTYRZxBQCUewY3SlOvaT/1FdJRxJM1FgHE7y1T3q7BfFRjXd/E7T/yQJkQ99lOaE/5lkQFsd9VyMLYhgj/f4fCeBXj/dovftjumCI+RDqUCouj4U8z9NiQ73ZxwKntbKVdNl95lmskjiOUCumDI6NYb0MlaLmXkVdRJtHU/NWCyIdBXY4KGhOsORi/dJSPDXbn0Fc53sQCCIHJMretH28G0+HaT0rtTfNrgXCXikc9MS0W3UFHEIQSM40bq1kmxpW2bvRLbmbWlHBd2QAWXCeFYTDmhT2OlmLEizVsVp/7IuZ9MwKa85YL39LaniaUHJ+q/51sE8oRoKntY9mskFdvBcGwiTM0y5GfCuYYqS3KJ44uTGr+G3/HscnykQO95uVwIN0U+h+QsOROmHqosTZn3mb/mOhcMh3KMVTZFH4cm+fOcH+OLV2sE2psZO7ZPQHWW/Ml1AUIwlAoMBJongV71A2OxEY/0/aegXiNrlzLGzQvu4jFDrL4W+JU/fBiC669FRrP91fSoyUS4TNDF7w5ghnAjsGWMpRwzHdaPmlJlHHzmrMAWjcIUuck/YMojQrjDU8ipwjd0smxhzLv+ch50B4rIK20kkXbcp7RBRXtcEP3OXEYsESU5qcAg3zzDTjW8RL/0zwvqxfszSCmmnHMhRkyeYVkk6d2YYeCiXWnJ3/QmG0hzT+OcWBt/uUqhmoJU3smrr/qgnKHlyNBqb6+bPo6+grr5kGYqYNtlzeesfLzheanLcT+rBpopos+sgxBBWeJ1KEG9OZxn0UBeB8wpJwT3/9ppx+eNoTjemDtzzcQjrXknWcIDGpTDgZAII0lWj5WfbztmapVUTarwncPhu+9airzXR/0dMgJ2vqIUR85hJ4Vb0V5/TZh9jFc7DQC2ZqFf35HXC5cnejpdaEyoeoQEcu0N7JaSbEcwmCeA0NNExOTePnECdYhybkBaxY/Pn2HoXeRLYDdE7uAS7p7USOkM0AyUoyRFjMbEjcjkUpvSvv7ODWOrgWb8XavAqbDnBlMrIayaayOi3spsV4EtSBS3utqS5cFDGIOXxyZ8G07h1gXJFW4qGl/pT6pKZBWmMqYtP/zTgiUk/L4vNThQe87d2W1I8Y4CpZMVBLQY/KKPVh2bLt+HhneyWG35l7+5HN8dIj7uKCusQf6wVCp5t06WVN2dIE1aYamZojoatF5MktPupe71iSoZR/PshYfudig9sL5V88i4u5YzYuD4dsB0kckL9sgGky7BBp9xVZ93xP2amw8dN0ldPejfeaX0x6TxACXSExhZ6YRRp44CyDNYDQnp/bIRGT9Zr0qjRtPEXrKahe2BZMUMruKNfJDH8M0ebF5w0FHj2M5ALG5QfnGldnCDTLI/7hMzWr7penoB2vc3hO+sn7kRIRjf0nja5d3eM1iEtZXUM4SQoMhRegFWedXYoj2fvbYA6AewF++kFgwwxudq8OsRLLMd13Ckg8KOMQSRsDIpjklKnmQvyLfmHdfswcfch3UQjwLUnpaxcmfpvLrECfbBXyYf9jJW0xPw2a8ZwgJYFnAdrtYi1/NVEEX8SXOpAq5jYhpRuUEeE8Uxsgr3xDka4UzzvQkhoLPhBTdz3BtkY+7Kn/3d75h6vOikySYV6E5gu/Xu7LpQwXFujM30h7XVxCtKbImgwcVa09OdluKQIDuoMq/iGZyn3XVyOjLehe2DV5CilkU8fvtWxQp33F0hfzSucUTOERCWCcyVf4QP9dNw/qKp++bDKea1Ux2HIx0GJ+Am4vgeXWj087NZX7pM74lwkL/d9gdAIKzoZP/M06Q/54NwvDT+Fbb6IQnKDS3UGDvsQMh1aaWR9a2bY71FJV4ZFUTxCENoaIdBKIpJHCZSGuWv+zFu9oAkcAMnSV2+dL14jk8ZJWe2YxETW8oU7qE1co6RrKEPv2IMmF72bLx3lCMXcE8Nacksv4aWKJ+t8DhrdGgxzGy8ODcEyBBiF+BRcCNfoxyUhbmkbMQnYBjhKoJQSlExONvow76Qz3kE07aoYLcrmSYiQbu01r32yxhbG6mTBKYFJ4SVlcMbX8/bZdSXot9dAq3tJMU06Bzm5gbSdq7DZqQBxlUUTlVuOLvvRCaaXDSv62jdNakxkCLhJ8XBO3VfS9BeI/eO8XmUmIywEH+fhnIAdlKZnBlVgxqnzRpQPua8oACrKbl8c8Au490S4L4KR+/qYNFH2pZ+F93FyafHqRD59UPrdnbthm01COm2vVQjHL4vvoLVDmPmq5y2ZLluCzJFL5nvAIyhA8ttWnf3fFrGn2I27CUSt0lErxUIKsE09M453h+Wo8rSldsRwG8X5VnhxnhnHEkg6cM69hBJVcN5ChmvEswE1cO0pgCmb8JsVJ2DR1HDZyz5BzhoisCpFz/EmIlQ60SZRhrKwMrZ0UE4Umi3dVXr3MDoTd9h1GIm82YmR1y2EaKJt6jE02V9NLZypKlNl86yPkZhd7lWjA2dXYXno4/SSK5WURjnM6e7RnG8pyfYZwZLnH25dscPEUkcf+cBCLj9bF+O+W+YHs2iksfMvfPVb83LLD9GN9gdpZid3IByfSJW78Jf9jI1wk+lETLUd5av975cZLCR4eZBktEmVEPnGGRGUoiEoeBPG5UhgdatzwCcpHsNu2EDrYODW8CoWT++exWk6p7dEUtyyawWdXlvwC6LnYHxmSZiWUAYHTV0uSG10hm1HZmrOdLY+n/U8Sh9OOd8ubuuQnwgdy5ajiUiAx+ivvI/rHhT8xUKMxsOs4OiWlE9R5SDFk5AvXXdLdA18/xHJMoVvxRVyv9G7mz8XwycaF7nkbOJJESF1MN7TrijIaZmij92leqDzP7AUibWtWZh0wD+yu6XtU9ao2wjNuzoq7b1a+f0RuCVh1bXfi9tU1dpRqY/+EA/MceAxUSt1pYiVYJo3gtctK8x1dH/rfVw6PxZp3aq4hiOa5M7XV+5cgR24baBnBGeuXI6jm/42xALLBYXWSicJ4O0cVhcYfRPZkvx+qfyqYpKW0wIOI6rnzqJQtkOC5CD4sYpDDLmnUeJLizzIUy/y5Mg07y96bNcobOniuJbC40EqpZ/LhwLAExkrfvBpwcrAelXsIyloLc1J7gG5RQPfCs2qkK5v0b6g01LVbPf0mEQGnyLe+9WbsvZtFzSXKHeEejWMag1hU60h5b585WycAGW9JGU5ZIbqU1XmV3+iehzkvlvgnS19bcYXW1CzUdJWnSX4yjbNwVVl8X39PUxdEspCzRYBR7h6gXFmatXicvGX9v6fNV0i+Cp0JGXrwn+yUFGesJePvhKqi92C9+aqQWQqc4v6nLBhaN3P4jhHpytYwmVEBOY/LbzZ+zI4vd/YrcdFm/dErgol/k4+vtRnARKca+R5cQSuObuF0gTUoQ4PUWfN4A8M03ELwzUjphKN73hCramktTT2OIuwccv3fy5v5JtVjpinvs1UnuyRIKBPJznPxDaMPzo3SFwI8Pja+KvGjs9iAUg9z9p0osg7LgLAFNM09rC1MKIbfHJG3gGHEZYNA/nLzN4ZLBIKN/2pGwdTir189D5WpYFFOlYbGnXXHH/jtpryBwrQMxMsSRiad80J117w/Kkos5GkwoPLGEBCYk6OXAcG28JyRTz/lKUA0pgthWblorhay5DnLSigvXk/j9lE/A+KTKs4O8gAvZUSMh/PgIeqM2Y7OX2IpIDgY6fQv0eH+I3uku1cGwXlynX7p0d5Rajb/iPRY5qiKsAwPqqnil8YWDJrS4/VmF8zmUJAQOZdCKQVyn7KtWlpQVSda2a/TP1b5jGy9rsEztKCq2fyCBdDEl7EqznVZONJ9wQv0SxEOPQ8RIA+/7ln/pZ0VtRKPRdgIukyr6sa4Ahd/7TR5fS45ltXcw9/XH1HIlKifylg2YrW0tgEhphZVsgAbEmP6WwBe6+zpytW66Ply0QQMlQ2yvwWIsvO1fVsdvtVDBFiXXNIuTqRFr7tue9A264iMsGWbDOEnLJd89j/ClDcdY9CuDNqGjXt+k2e0v9W2jDbleUPsCXvJJJC8/PMKmrMYKCAzDJQotFQHpYeyFUm1n3ex7wY1rMY7I7h1v4yBlMpCqD+nO8I4i3VVh0W3LwlLXNxqAxaRlrlEmO6L4ci2jGuu0lwh4cPL20fxrr7nRd0RKWVN7bJjiLS/xBQYik1xcvnAPL2wvoJLl0yfh+5uMTcLdyWTH4uDAr1TyLJCJW6ObJgo/mpbGitDB0ao9OWovrpaDssfhEEIcPpollwNPWLu4wGcftM5pOmYU5I4TLY/4j6ypTn6Dm4NSIdOj0Ou5EH0Z7CbE+4nYz5yDLH4rISlL2Rczb9khY5N4AEDiwqN3rriAcR73/3cVStoFvuzvDvBTNJh3kbzfkX5mau7nSQtl28keGH7Gbiq46GGFEVSC5D44e/MWVwheG4dtNX5DihOnFLgcXwDIaNyR/AwZxO23a4jcm4XCMKIhMYiFFmfqE+50t1XD+/CGtc0kCV+ozdVdqmv7Vbk6vVRg2Fo+1M/wx9NPsqljzPbCI4SK6uiRkxYbJJnCr+KkKNffrAC93+Lpx41Or7AN5X8e9e7XryFGaZzs0w5viZNC65Aw+qNZ4hFtX1R8LA/ns5cgdmVs4o0LohiuYRzr3iGJ1/76U7SNEeyuMj7YAB6OfWH2Ucr6TSOWFE+nJXtpg3804KknYy5DSO4DHTeKn4+R8b6NwBO0abYl8BpnTIzWn++B828/8F6CPEdw369u/OCtBbac6FT8bqCloAVEyj/GbQRDxcY6Y43VIkU090Z3PE+Q7YZFQnX5z9wlfeDEvYI48JyzNEYgmrzD8hYsCiIFgBag6KJTCTLVzZGa1vlvVZon2QbBX1rN04a4OX+oMscD43/1gRTEfupxJyo87DoyYlN+n73yOs4GLs6iGVgOKAckJPXFfqiUESVKp3Jpfns+bGlb6HufzEHagsk+2hqG/SQsrVQ2z/uETUcNyHVWNGAevFEdpfWDgTqLb8rATpBA5qR2cE68lDXClEflAEYns1h02jWJPUIXfhnyc5P4NW3eVuxgvZzYpp2+uV+5aVesgB8WteWeNQPZ6kQCJ5hgeX+Vmp//N/tDb6jbMQaUA//Ugf2kdFJZSkKPmyahMQFFFqYqSyBE2DA76mhRAPIFSODu1rMRXhwvCjeYmpi4e2HGgUhjkOKniU/oUwLbEcXFs6aB8H1DsgE9wfPFD3cRC1moxAqMI6EncaGJQUb7E1sfqn/6nhqiD15ejK5lWuaRyHnmqHjTXeNH8OdFq2+KjU7PQGqwmEdEecDsrJg2SU70ljlGgkZCJIKxqlgR3L21PtJdRylAHKVBrmFaxOXgDrHLuYdbOguqIaRCrLmVVbUu/FYgK3MZTDRuG4u5b/gtU62yIEuu67qaobNRBG4Y9rTxbEhKmx9XUUowgkOjkUq/jIVUh1mSSeGND2VUhFigszp6pfR7+wAaB3+vYFw4s7VcJVazuWwWH/802VRzfMtmeLJ+Z4GohoBQ2z7WgNAwoIEwRR+G1CoBAgVfMk2UW1Wi75AQB3pkqj0HDpsHbyHSL0UZsJ2MsQ6AFdnIX2dNq+85ku+7PXGJkg+r40PV2O/u2O3kG7OuO8quqFbNXEvaPeMpa7Fn5q+gQ70BBbv78FI3qpt5krPWWw1PviKgHzRql1y6Z9Rj+4duv1fNGbAUAeq9/XHKRPuOaLdpeFJ4Rdp6PT6syz58xvlYYssp3eBRAYsDIBPCW5ifpd5+wYxoOmf+8kkaJZpR5fEeXvQZlOfjYoE0/g8hB2ZwkMNiHFFoT/Ih4bJY4w7c/F4zA9JrI6wSLgz5l1vXXPVneBvoNKKa1udgabzadwTwTsjOkHjyfZWNQMCtYOezTjfn69ynNowwaHaYTSC7Le5QC3WXKovYUh7JYs7kBonFW2egekLuHp/2+ttX9v0tVTtBkoMH617SToMBq/n7C93DJhuvby2w8lZN4UMS+qfCAI6gAUJDPvuzMIB09XMa0X6thWIlYoEpmqoqzRWksbh6lyFe4uh5iQQzzwgvtlj264n7CpxbsRKoW19/p9u+a822gYXne56PdJ8yPmFNRaqw9eFit0aiR8Z+GAY4RjWrzU5TJc+oHoliEIpZb6kvynqVchSLwCXiNlmgd/EhgqHmCXs3A/64NJ/dXVWLMNmuV63EkjZ2quAM026xiERbrTHPu2LiY2tbtnQy9hfMPQM+UO3NMv7/0EdzPl0nWdsEcsekIblVJHYRP1AI9soQn6KmTQQGj8RLqGikdOJQVKW/rEfOdo2f2vKWPyRkT6ggPJKtgUKXbg8mHYdZYEDjUaRPxLZgXY2ma8w15TFnXM5GnxtwuYnVmCd67karua2GiDPQe2eilookUhRhzTbYW4HtiNG8JyMCjwnSTylhFuO5D6JyvCwuO4E2mYaK8Ibm9LXniLMl41j/8cyDkYuRmAf+L8W0fcAyfK2BHInsaapoSP7aZoTrHEiiYzhSMrWx6B/9bnyA8GpU2yj5QQYMLs1fHF5Yycjck1196JoI9soZzdWKRXInDM1sAvTaTbO5J9cmnaOUvqX54blNWTgqUHFwc8rctCDVR1JUjPvvnbyRPRwyGYF0fHugV921aP7JhwsGzdMOtXdFmdk1im9udCMbGIXGdBrvjTxxlYV6atVcKJ7AiXn8yfZ3BFMyHwdcwSTnP9+mZDMvUli1eYLzSmx+wbLEnG0BqYLdZbZwce/7BEgvnURfMhXkMxoi8N8ggeaGpAW153W4r88NpOof20C201A/pXCMo4rSOdgoQ3U1MDcvp6tCTWiU18E4/vo9L+yronJDCkLtgRNkwE3DUvDe8wnJQJBHJ4a7bk2oaNu/Z87tUBtzndW1/PEgPS3QvKy00Kvxpfvtrg+46loqasEqFl14Vjp2n+mWSc3zj2kEUZmAkr75mEWHsfuYIljspcPhN1FYUTlAJx3iOr7qIii6PZQj3MU7ILNekGDW80YYWCVsxnICGjrQzgSaQD1Qwu7Kw3uow6qHNoymaHaXnKDD/uD7D5T+whZ60CiBwkLi4FYGTNFTwyL+JX4mNUuWds2olxrMgxYviHwykzSPml6WMxB6leokk/QLFJKJn6SA/tmJpHWANkmRF6vAzKkooecz1XoRx19vzriRzH5ErDe8VEtxDSw7+AH12ASjDX7rmNr7hx0DNrmdC5LLlgCbmBQMMEOQ9EZRjBb2StkU+Wn/3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)

**Figure : Expected output for object detection and display using RTSP stream–Preview**

## Pipeline flow

The following table lists the plugins used in the object detection pipeline:

| Plugin | Description |
| --- | --- |
| Camera source: [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-80022-50/topic/qtiqmmfsrc.html) | <ul class="simple"><br><li><p>Captures the live stream from camera.</p></li><br><li><p>Uses tee to split the stream for inferencing.</p></li><br></ul> |
| File source: filesrc | <ul class="simple"><br><li><p>Captures the video stream using filesrc, followed by qtdemux, which demultiplexes the stream.</p></li><br><li><p>Uses tee to split the stream for inferencing.</p></li><br></ul> |
| RTSP source: rtspsrc | <ul class="simple"><br><li><p>Captures the RTSP stream using rtspsrc, followed by rtph264depay for video extraction.</p></li><br><li><p>Uses tee to split the stream for inferencing.</p></li><br></ul> |
| USB camera source: v4l2src | <ul class="simple"><br><li><p>Captures the live stream from USB camera.</p></li><br><li><p>Uses tee to split the stream for inferencing.</p></li><br></ul> |
| h264parse | Parses the H.264 video. |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-80022-50/topic/v4l2h264dec.html) | Decodes the video. |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlvconverter.html) | <ol class="arabic simple"><br><li><p>Receives the video stream on its sink pad.</p></li><br><li><p>Performs the following preprocessing on the stream data. This preprocessing is done when the model expects floating-point values as input.</p></li><br><li><p>Color conversion</p></li><br><li><p>Scaling (up or down)</p></li><br><li><p>Normalization</p></li><br><li><p>Converts the preprocessed video stream to a tensor stream on its source pad.</p></li><br></ol><br><br>The tensor stream is used for inferencing in the later stages of the pipeline. |
| Inferencing plugins:<br><ul class="simple"><br><li><p><a href="https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlsnpe.html"><span class="doc">qtimlsnpe</span></a></p></li><br><li><p><a href="https://docs.qualcomm.com/doc/80-80022-50/topic/qtimltflite.html"><span class="doc">qtimltflite</span></a></p></li><br><li><p><a href="https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlqnn.html"><span class="doc">qtimlqnn</span></a></p></li><br></ul> | <ol class="arabic simple"><br><li><p>After the inference runtime receives the tensor stream on its sink pad, it runs the inference.</p></li><br><li><p>Produces a tensor stream with the inference results on its source pad.</p></li><br></ol> |
| [qtimlpostprocess](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlpostprocess.html) | Handles inference results from any object detection model. 1. Applies a threshold to the chosen number of results. 2. Loads the YOLO (YOLOv5, YOLOv8, or YOLO-NAS) module. 3. Produces video frames with only bounding boxes that can be overlaid on objects. 4. Sends these processed frames to the sink pad of qtivcomposer. |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-80022-50/topic/qtivcomposer.html) | <ol class="arabic simple"><br><li><p>Composes frames with contents from its sink pads.</p></li><br><li><p>Pushes the GStreamer buffers containing these composed frames to its source pad.</p></li><br></ol> |
| [Waylandsink](https://docs.qualcomm.com/doc/80-80022-50/topic/waylandsink.html) | <ol class="arabic simple"><br><li><p>Waylandsink submits the video stream received on its sink pad to Weston.</p></li><br><li><p>Weston renders the video stream on a local display.</p></li><br></ol> |
| filesink | Receives the video stream on sink pad and saves it as an H.264-encoded MP4 file. |
| qtirtspbin | <ol class="arabic simple"><br><li><p>Serves as a network sink.</p></li><br><li><p>Transmits UDP packets to the network.</p></li><br></ol> |

## Config JSON field description

The different parameters available to configure the JSON file and run the use case are as follows:

Table : Field description–config_detection.json file

| Field | Values/description |
| --- | --- |
| **ml-framework** | Use one of the following models:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">snpe</span></code>: Qualcomm Neural Processing SDK</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">tflite</span></code>: LiteRT</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">qnn</span></code>: Qualcomm AI Engine direct</p></li><br></ul> |
| **yolo-model-type** | Run the `yolov5`, `yolov8`, `yolox`, and `yolonas` models respectively. See [Sample model and label files](https://docs.qualcomm.com/doc/80-80022-50/topic/vision_ai_demo_app.html#section-ohr-1qq-32c). |
| **runtime** | Use one of the following runtimes:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">cpu</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">gpu</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">dsp</span></code></p></li><br></ul> |
| **Input source** | Use one of the following input sources:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">camera</span></code>: Primary (0) or secondary (1).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">file-path</span></code>: The directory path to the video file.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">rtsp-ip-port</span></code>: The address of the RTSP stream: <em>rtsp://&lt;ip&gt;:&lt;port&gt;/&lt;stream&gt;</em></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">enable-usb-camera</span></code>: Set to TRUE or FALSE.</p></li><br></ul> |
| **output-ip-address** | Output server IP address. |
| **port** | Output server port. |
| **output-type** | Use one of the following output-type:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">waylandsink</span></code>: To display output on Wayland.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">filesink</span></code>: To store output in file.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">rtspsink</span></code>: To stream output on server.</p></li><br></ul> |
| **snpe-tensors** | `["output-tensor-name","output-tensor-name"]` |
| **USB camera video-format and resolution** | Use one of the following video-format:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">nv12</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">yuy2</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">mjpeg</span></code></p></li><br></ul><br><br>Use one of the following resolution fields:<br><ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">width</span></code>: Input USB camera source resolution width.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">height</span></code>: Input USB camera source resolution height.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">framerate</span></code>: Input USB camera source framerate.</p></li><br></ul> |
| **output-file** | Output filename. The default output file is `output_object_detection.mp4`. |

## Known issues

Tab Config #2
Tab Config #1

- Lag is observed in object detection with camera source and file source as quantized models aren't supported in the TFlite IM SDK framework.
- The application may intermittently hang during the `gst_deinit` phase.
- The GPU delegate doesn't function in the QNN and TFLite IM SDK frameworks.
- Bounding box isn't observed with the camera source input.

- USB camera input isn't supported.
- Video encoding use case isn't supported.

## Related information

- [Object detection and display with LiteRT](https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-display.html)
- [Object detection and encode with LiteRT](https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-encode.html)
- [Object detection and display with Neural Processing SDK](https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.html)
- [Object detection and encode with Neural Processing SDK](https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.html)

Last Published: May 14, 2026

Source: [https://docs.qualcomm.com/doc/80-80022-50/topic/gst-ai-object-detection.html](https://docs.qualcomm.com/doc/80-80022-50/topic/gst-ai-object-detection.html)