# Object detection and encode with LiteRT The use cases use a YOLOX LiteRT model to identify the object in a scene. The use case is to either overlay or compose the bounding boxes over the detected objects, and then encode this stream as an H.264 bitstream. ## Use qtivoverlay plugin to apply bounding box 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/output_video.mp4 split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external \ external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/yolox_quantized.tflite ! queue ! \ qtimlpostprocess settings="{\"confidence\": 75.0}" results=10 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 the 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 the stream as a 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** The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-80022-50/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/80-80022-50/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 an 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** | | [qtimltflite](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimltflite.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](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlpostprocess.html) |

  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/80-80022-50/topic/qtimetamux.html) |

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


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


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


| | [qtivoverlay](https://docs.qualcomm.com/doc/80-80022-50/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/80-80022-50/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/output\_video.mp4* file. | | Playback | Pull *output\_video.mp4* from the host computer and play it on a media player: `scp root@\:/etc/media/output_video.mp4 ` | ## Use qtivcomposer to mix original frame with bounding box 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/output_video.mp4 \ split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \ \external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/yolox_quantized.tflite ! queue ! \ qtimlpostprocess settings="{\"confidence\": 75.0}" results=10 module=yolov8 labels=/etc/labels/yolox.json ! \ video/x-raw,format=BGRA,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. Using qtivcomposer, compose bounding boxes over the objects detected and the original video stream. 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** The following table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-80022-50/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/80-80022-50/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** | | [qtimltflite](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimltflite.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](https://docs.qualcomm.com/doc/80-80022-50/topic/qtimlpostprocess.html) |

  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 the sinkpad of qtivcomposer.




| | [qtivcomposer](https://docs.qualcomm.com/doc/80-80022-50/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 video streams processed from its sinkpads.


| | [v4l2h264enc](https://docs.qualcomm.com/doc/80-80022-50/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/output_video.mp4` file. | | Playback | Pull *output\_video.mp4* from the host computer and play it on a media player: `scp root@\:/etc/media/output_video.mp4 ` | Last Published: May 14, 2026 [Previous Topic Object detection and display with LiteRT](https://docs.qualcomm.com/bundle/publicresource/80-80022-50/topics/single-camera-stream-with-object-detection-and-display.md) [Next Topic Image segmentation and display with LiteRT](https://docs.qualcomm.com/bundle/publicresource/80-80022-50/topics/single-camera-stream-with-image-segmentation-and-display.md) Source: [https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-encode.html](https://docs.qualcomm.com/doc/80-80022-50/topic/single-camera-stream-with-object-detection-and-encode.html)