# Object detection and encode with LiteRT

Source: [https://docs.qualcomm.com/doc/80-70018-50/topic/single-camera-stream-with-object-detection-and-encode.html](https://docs.qualcomm.com/doc/80-70018-50/topic/single-camera-stream-with-object-detection-and-encode.html)

The use cases use a YOLOv5 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:

    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 ! qtimltflite delegate=external \
    external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/yolov5.tflite ! queue ! \
    qtimlvdetection threshold=75.0 results=10 module=yolov5 labels=/etc/labels/yolov5.labels \
    constants="YoloV5,q-offsets=<3.0>,q-scales=<0.005047998391091824>;" ! text/x-raw ! queue ! metamux.Copy to clipboard

To stop the use case,  use CTRL + C.

Figure : Pipeline for bounding box overlay and encode
                
                ![](data:image/png;base64,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)

The 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.

The table provides the sequential processing stages of the pipeline execution:

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70018-50/topic/qtiqmmfsrc.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_l2f_zgm_vbc"><br>                                    <li class="li">Collects the video stream (source) and creates two copies of<br>                                        the source:<ul class="ul" id="single-camera-stream-with-object-detection-and-encode__ol_m2f_zgm_vbc"><br>                                            <li class="li">One stream is sent to qtimetamux plugin to retain<br>                                                the video stream.</li><br><br>                                            <li class="li">The other stream is sent to an ML inferencing<br>                                                pipeline.</li><br><br>                                        </ul><br></li><br><br>                                </ol> |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvconverter.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_xsf_q5l_vbc"><br>                                    <li class="li">Receives the video stream on its sink pad.</li><br><br>                                    <li class="li">Performs preprocessing:<ul class="ul" id="single-camera-stream-with-object-detection-and-encode__ul_ff2_twl_vbc"><br>                                            <li class="li">Color conversion</li><br><br>                                            <li class="li">Scaling down/up</li><br><br>                                            <li class="li">Normalization on the stream data when the model<br>                                                expects the floating point values as an input</li><br><br>                                        </ul><br></li><br><br>                                    <li class="li">Converts the video stream to a tensor stream on its source<br>                                            pad.<p class="p">The object detection model uses this tensor<br>                                            stream for inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimltflite.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_ufn_2lm_vbc"><br>                                    <li class="li">Loads the object detection model.</li><br><br>                                    <li class="li">Modifies the graph for the chosen delegate.</li><br><br>                                    <li class="li">Receives the tensor stream on its sinkpad.</li><br><br>                                    <li class="li">Runs the inference and produces a tensor stream with the<br>                                        object detection results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvdetection.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_ky5_grn_vbc"><br>                                    <li class="li"> Receives the inference tensors from the object detection<br>                                        model. </li><br><br>                                    <li class="li">Converts the inference tensors on its sinkpad into formats<br>                                        like video or text that the multimedia plugins can process<br>                                        later.</li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results. </li><br><br>                                    <li class="li">Loads the corresponding modules for detection models. <p class="p">In<br>                                            this use case, qtimlvdetection does the following:<br>                                            </p><ol class="ol" type="a" id="single-camera-stream-with-object-detection-and-encode__ol_jcd_wnk_5bc"><br>                                            <li class="li">Loads the YOLOv5 submodule. </li><br><br>                                            <li class="li">Produces results as structures of text.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtimetamux.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtimetamux](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimetamux.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_ll3_x5l_vbc"><br>                                    <li class="li">Receives video stream and text stream with bounding box<br>                                        results corresponding to the video stream on its<br>                                        sinkpads.</li><br><br>                                    <li class="li">Produces GST buffers with contents of the video stream from<br>                                        its sink pad.</li><br><br>                                    <li class="li">Adds bounding boxes as GstVideoRegionOfInterest from data<br>                                        sinkpad to GST buffers meta (meta muxing) on its source<br>                                        pad.</li><br><br>                                </ol> |
| [qtivoverlay](https://docs.qualcomm.com/doc/80-70018-50/topic/qtioverlay.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_wst_y5l_vbc"><br>                                    <li class="li">Receives the multiplexed stream.</li><br><br>                                    <li class="li">Overlays the bounding boxes on the VideoFrame using CL. </li><br><br>                                    <li class="li">Produces GST buffers with overlays in its source pad.</li><br><br>                                </ol> |
| [v4l2h264enc](https://docs.qualcomm.com/doc/80-70018-50/topic/v4l2h264enc.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_wsc_bsn_vbc"><br>                                    <li class="li">Applies parameters to each frame of the video stream it's<br>                                        receiving on its sinkpad.</li><br><br>                                    <li class="li">Encodes it into bitstream and sends it over its<br>                                        sourcepad.</li><br><br>                                </ol> |
| h264parse | Adds more information about the bitstream to the GStreamer buffer<br>                                meta. |
| mp4mux | Receives these buffers and creates containers with format<br>                                specification buffers. |
| **Output** | **Output** |
| Filesink | Stores the resulting stream in a<br>                                    /opt/video.mp4 file. |
| Playback | Pull video.mp4 from the host computer and<br>                                play it on a media player:<br>`scp root@<IP address of<br>                                        target device>:/opt/ <destination<br>                                directory>` |

## Use qtivcomposer to mix original frame with bounding box mask

Run the use case:

    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 ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so \
    \external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/etc/models/yolov5.tflite ! queue ! \
    qtimlvdetection threshold=75.0 results=10 module=yolov5 labels=/etc/labels/yolov5.labels \
    constants="YoloV5,q-offsets=<3.0>,q-scales=<0.005047998391091824>;" ! video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer.Copy to clipboard

To
                stop the use case, use CTRL + C.

Figure : Pipeline for bounding box mask and encode with qtivcomposer
                
                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)

The 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.

The table provides the sequential processing stages of the pipeline execution:

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70018-50/topic/qtiqmmfsrc.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_wqx_ntn_vbc"><br>                                    <li class="li">Collects the video stream (source) and creates two copies of<br>                                        the source:<ul class="ul" id="single-camera-stream-with-object-detection-and-encode__ol_xqx_ntn_vbc"><br>                                            <li class="li">One stream is sent to qtimetamux plugin to retain<br>                                                the video stream.</li><br><br>                                            <li class="li">The other stream is sent to an ML inferencing<br>                                                pipeline.</li><br><br>                                        </ul><br></li><br><br>                                </ol> |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvconverter.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_yqx_ntn_vbc"><br>                                    <li class="li">Receives the video stream on its sink pad.</li><br><br>                                    <li class="li">Performs preprocessing:<ul class="ul" id="single-camera-stream-with-object-detection-and-encode__ul_zqx_ntn_vbc"><br>                                            <li class="li">Color conversion</li><br><br>                                            <li class="li">Scaling down/up</li><br><br>                                            <li class="li">Normalization on the stream data when the model<br>                                                expects the floating point values as input</li><br><br>                                        </ul><br></li><br><br>                                    <li class="li">Converts the video stream to a tensor stream on its source<br>                                            pad.<p class="p">The object detection model uses this tensor<br>                                            stream for inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimltflite.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_arx_ntn_vbc"><br>                                    <li class="li">Loads the object detection model.</li><br><br>                                    <li class="li">Modifies the graph for the chosen delegate.</li><br><br>                                    <li class="li">Receives the tensor stream on its sinkpad.</li><br><br>                                    <li class="li">Runs the inference and produces a tensor stream with the<br>                                        object detection results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70018-50/topic/qtimlvdetection.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_brx_ntn_vbc"><br>                                    <li class="li"> Receives the inference tensors from the object detection<br>                                        model. </li><br><br>                                    <li class="li">Converts the inference tensors on its sinkpad into formats<br>                                        like video or text that the multimedia plugins can process<br>                                        later.</li><br><br>                                    <li class="li">Applies the threshold to the chosen number of results. </li><br><br>                                    <li class="li">Loads the corresponding modules for detection models. <p class="p">In<br>                                            this use case, qtimlvdetection does the following:<br>                                            </p><ol class="ol" type="a" id="single-camera-stream-with-object-detection-and-encode__ol_crx_ntn_vbc"><br>                                            <li class="li">Loads the YOLOv5 submodule. </li><br><br>                                            <li class="li">Produces video frames with only bounding boxes that<br>                                                can be overlaid on objects.</li><br><br>                                            <li class="li">Sends them to the sinkpad of qtivcomposer.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70018-50/topic/qtivcomposer.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_wgh_rtn_vbc"><br>                                    <li class="li">Receives the original video stream and video stream with<br>                                        bounding boxes on its sinkpads</li><br><br>                                    <li class="li">On its sourcepads, produces content that's composed of video<br>                                        streams processed from its sinkpads.</li><br><br>                                </ol> |
| [v4l2h264enc](https://docs.qualcomm.com/doc/80-70018-50/topic/v4l2h264enc.html) | <ol class="ol" id="single-camera-stream-with-object-detection-and-encode__ol_frx_ntn_vbc"><br>                                    <li class="li">Applies parameters to each frame of the video stream it's<br>                                        receiving on its sinkpad.</li><br><br>                                    <li class="li">Encodes it into bitstream and sends it over its<br>                                        sourcepad.</li><br><br>                                </ol> |
| h264parse | Adds more information about the bitstream to the GStreamer buffer<br>                                meta. |
| mp4mux | Receives these buffers and creates containers with format<br>                                specification buffers. |
| **Output** | **Output** |
| Filesink | Stores the resulting stream in a<br>                                    /etc/media/video.mp4 file. |
| Playback | Pull video.mp4 from the host computer and<br>                                play it on a media player:<br>`scp root@<IP address of<br>                                        target device>:/etc/ <destination<br>                                directory>` |

**Parent Topic:** [LiteRT use cases](https://docs.qualcomm.com/doc/80-70018-50/topic/tensorflow-lite-use-cases.html)

Last Published: Jan 30, 2026

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