# Object detection and encode with Neural Processing SDK Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.html](https://docs.qualcomm.com/doc/80-70022-50/topic/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.html) The use cases implement a yolox.dlc object detection model with Qualcomm Neural Processing SDK to identify an object from a camera stream. The use case is to overlay or compose the bounding boxes over the detected objects, and then encode the stream as a H.264 bitstream. Download [YOLOX](https://aihub.qualcomm.com/iot/models/yolox?searchTerm=yolox%29) Qualcomm AI runtime w8a8 precision model from AI hub. The YOLOX model uses the YOLOv8 postprocessing module. Note: For Ubuntu Server, `sudo` access is necessary to write the encoded stream to the `/etc/media` folder. ## Use qtivoverlay plugin to apply detection overlay Run the use case on the target device: gst-launch-1.0 -e \ qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! tee name=split \ split. ! queue ! qtimetamux name=metamux ! queue ! qtivoverlay ! queue ! v4l2h264enc capture-io-mode=4 output-io-mode=5 ! h264parse ! queue ! mp4mux ! queue ! filesink location=/etc/media/video.mp4 \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/models/yolox-yolo-x-w8a8.dlc layers="" ! queue ! \ qtimlpostprocess settings="{\"confidence\": 70.0}" results=5 module=yolov8 labels=/etc/labels/yolox.json ! text/x-raw ! queue ! metamux.Copy to clipboard To stop the use case, use CTRL + C. The following figure shows the flow of the use case execution: 1. Identify object scenes from a video stream, which is coming through a camera source. 2. Overlay bounding boxes over the detected objects using overlaylib. 3. Encode this stream as an H.264 bitstream. 4. Multiplex the stream in an MP4 container and stored as an MP4 file. Figure : Pipeline for bounding box overlay and encode Qualcomm Open source camsrc tee qtimlvconverter qtimetamux qtioverlay v4l2h264enc h264parse mp4mux filesink qtimlsnpe qtimlpostprocess The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70022-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The object detection model uses this tensor
    stream for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces a tensor stream with the
    object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | qtimlpostprocess |

  1. Receives the inference tensors from the object detection
    model.


  2. Converts the inference tensors on its sinkpad into formats
    like video or text that the multimedia plugins can process
    later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.

    In
    this use case, qtimlpostprocess does the following:


    1. Loads the YOLOv8 submodule.


    2. Produces results as structures of text.


    3. Sends them to the sinkpad of qtimetamux.





| | [qtimetamux](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimetamux.html) |

  1. Receives video stream and text stream with the bounding box
    results corresponding to the video stream on its
    sinkpads.


  2. Produces GST buffers with the contents of the video stream
    from its sink pad.


  3. Adds the bounding boxes as
    GstVideoRegionOfInterest from data
    sinkpad to GST buffers meta (meta muxing) on its source
    pad.


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-70022-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the bounding boxes on the VideoFrame using CL.


  3. Produces GST buffers with overlays in its source pad.


| | [v4l2h264enc](https://docs.qualcomm.com/doc/80-70022-50/topic/v4l2h264enc.html) |

  1. Applies parameters to each frame of the video stream it's
    receiving on its sinkpad.


  2. Encodes it into bitstream and sends it over its
    sourcepad.


| | h264parse | Adds more information about the bitstream to the GStreamer buffer
meta. | | mp4mux | Receives these buffers and creates containers with format
specification buffers. | | **Output** | **Output** | | Filesink | Stores the resulting stream in a
/etc/media/video.mp4 file. | | Playback | Pull video.mp4 from the host computer and
play it on a media player:
`scp root@ target device>:/etc/media/video.mp4 directory>` | ## Use qtivcomposer to mix original frame with detection mask Run the use case on the target device: gst-launch-1.0 -e \ qtiqmmfsrc name=camsrc ! video/x-raw,format=NV12_Q08C,width=1280,height=720,framerate=30/1 ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer ! queue ! video/x-raw,format=NV12,width=1920,height=1080,interlace-mode=progressive,colorimetry=bt601 ! \ v4l2h264enc capture-io-mode=4 output-io-mode=5 ! h264parse ! queue ! mp4mux ! queue ! filesink location=/etc/media/video.mp4 \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/etc/models/yolox-yolo-x-w8a8.dlc layers="" ! queue ! \ qtimlpostprocess settings="{\"confidence\": 70.0}" results=5 module=yolov8 labels=/etc/labels/yolox.json ! video/x-raw,width=640,height=360 ! queue ! mixer.Copy to clipboard To stop the use case, use CTRL + C. The following figure shows the flow of the use case execution: 1. Identify object scenes from a video stream, which is coming through a camera source. 2. Compose bounding boxes over objects detected and original video stream using qtivcomposer. 3. Encode this stream as an H.264 bitstream. 4. Multiplex the stream in an MP4 container and stored as an MP4 file. Figure : Pipeline for bounding box mask and encode with qtivcomposer Qualcomm Open source camsrc tee qtimlvconverter qtivcomposer v4l2h264enc h264parse mp4mux filesink qtimlsnpe qtimlvdetection The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70022-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:

    • One stream is sent to qtimetamux plugin to retain
      the video stream.


    • The other stream is sent to an ML inferencing
      pipeline.





| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:

    • Color conversion


    • Scaling down/up


    • Normalization on the stream data when the model
      expects the floating point values as input





  3. Converts the video stream to a tensor stream on its source
    pad.

    The object detection model uses this tensor
    stream for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70022-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Runs the inference and produces tensor stream with the
    object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | qtimlpostprocess |

  1. Receives the inference tensors from the object detection
    model.


  2. Converts the inference tensors on its sinkpad into formats
    like video or text that the multimedia plugins can process
    later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.

    In
    this use case, qtimlpostprocess does the following:


    1. Loads the YOLOv8 submodule.


    2. Produces video frames with only bounding boxes that
      can be overlaid on objects.


    3. Sends them to sinkpad of qtivcomposer.





| | [qtivcomposer](https://docs.qualcomm.com/doc/80-70022-50/topic/qtivcomposer.html) |

  1. Receives the original video stream and video stream with
    bounding boxes on its sinkpads


  2. On its sourcepads, produces content that's composed of the
    video streams processed from its sinkpads.


| | [v4l2h264enc](https://docs.qualcomm.com/doc/80-70022-50/topic/v4l2h264enc.html) |

  1. Applies parameters to each frame of the video stream its
    receiving on its sinkpad.


  2. Encodes it into bitstream and sends it over its
    sourcepad.


| | h264parse | Adds more information about the bitstream to the GStreamer buffer
meta. | | mp4mux | Receives these buffers and creates containers with format
specification buffers. | | **Output** | **Output** | | Filesink | Stores the resulting stream in a
/etc/media/video.mp4 file. | | Playback | Pull video.mp4 from the host computer and
play it on a media player:
`scp root@ target device>:/etc/media/video.mp4 directory>` | ## Known issue The current model in AI hub isn't giving the expected output. The issue will be fixed in a future release. **Parent Topic:** [Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/doc/80-70022-50/topic/qualcomm-neural-processing-sdk-use-cases.html) Last Published: Feb 20, 2026 [Previous Topic Object detection and display with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.md) [Next Topic Image segmentation and display with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70022-50/topics/single-camera-stream-with-image-segmentation-and-display-with-deeplabv3-quantized.md)