# Pose estimation and encode with TFLite

Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-pose-estimation-and-encode.html](https://docs.qualcomm.com/doc/80-70015-50/topic/single-camera-stream-with-pose-estimation-and-encode.html)

The use cases use the PoseNet TFLite model to process a single camera stream with
        pose estimation and encode the stream as an H.264 bitstream.

## **Variant 1: Use qtioverlay plugin to apply pose estimation overlay**

Run the use
                case:

    setprop persist.overlay.use_c2d_blit 2Copy to clipboard

    gst-launch-1.0 -e \
    qtiqmmfsrc name=camsrc ! video/x-raw\(memory:GBM\),format=NV12,width=1280,height=720,framerate=30/1,compression=ubwc ! queue ! tee name=split \
    split. ! queue ! qtimetamux name=metamux ! queue ! qtioverlay ! queue ! v4l2h264enc capture-io-mode=5 output-io-mode=5 ! h264parse ! queue ! mp4mux ! queue ! filesink location=/opt/video.mp4 \
    split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/opt/posenet_mobilenet_v1.tflite ! queue ! qtimlvpose threshold=51.0 results=2 module=posenet labels=/opt/posenet_mobilenet_v1.labels constants="Posenet,q-offsets=<128.0,128.0,117.0>,q-scales=<0.0784313753247261,0.0784313753247261,1.3875764608383179>;" ! text/x-raw ! queue ! metamux.Copy to clipboard

To stop the use case, press CTRL + C.

Figure : Pipeline for pose estimation and encode using qtioverlay
                
                ![](data:image/png;base64,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)

The figure shows the flow of the use case execution:

1. Identify poses of people in the scenes from the video stream coming through the
                    camera source.
2. Overlay the available poses using overlaylib.
3. Encode the stream as an H.264 bitstream.
4. Multiplex the stream in an MP4 container and store it as an MP4 file.

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

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70015-50/topic/qtiqmmfsrc.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-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-pose-estimation-and-encode__ol_m2f_zgm_vbc"><br>                                            <li class="li">One stream is sent to the qtimetamux plugin to<br>                                                retain 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/80-70015-50/topic/qtimlvconverter.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-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-pose-estimation-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 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 PoseNet model uses this tensor stream for<br>                                            inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimltflite.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_bwn_s5l_vbc"><br>                                    <li class="li">Loads the PoseNet 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>                                        pose estimation results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvpose](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvpose.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_gr1_w5l_vbc"><br>                                    <li class="li">Receives the inference tensors from a PoseNet model on its<br>                                        sinkpad.</li><br><br>                                    <li class="li">Converts the tensors into formats such as video or text that<br>                                        the multimedia plugins can process 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 of the pose estimation<br>                                        models. <p class="p">In this use case, qtimlvpose does the<br>                                            following:</p><ol class="ol" type="a" id="single-camera-stream-with-pose-estimation-and-encode__ol_lyh_txn_vbc"><br>                                            <li class="li">Loads the PoseNet 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/80-70015-50/topic/qtimetamux.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_ll3_x5l_vbc"><br>                                    <li class="li">Receives the video stream and text stream with pose results<br>                                        corresponding to video stream on its sinkpads.</li><br><br>                                    <li class="li">Produces GST buffers with the contents of video stream on<br>                                        its sink pad.</li><br><br>                                    <li class="li">Adds poses from data sinkpad to GST buffer meta (meta<br>                                        muxing) on its source pad.</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70015-50/topic/qtioverlay.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_wst_y5l_vbc"><br>                                    <li class="li">Receives the multiplexed stream.</li><br><br>                                    <li class="li">Overlays the poses 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/80-70015-50/topic/v4l2h264enc.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_b25_j14_vbc"><br>                                    <li class="li">Applies parameters to each frame of the video stream it is<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 additional information corresponding to the bitstream to<br>                                GStreamer buffer meta. |
| mp4mux | Receives the buffers and creates containers format specification<br>                                buffers. |
| **Output** | **Output** |
| Filesink | Stores the resulting stream in a<br>                                    /opt/video.mp4 file. |
| Playback | Pull video.mp4 from the host machine and<br>                                play it on a media player:<br>`scp root@<IP address of<br>                                        target device>:/opt/ <destination<br>                                directory>` |

## Variant 2: Use qtivcomposer to mix original frame with pose estimation
                mask

Run the use
                case:

    gst-launch-1.0 -e --gst-debug=2 \
    qtiqmmfsrc name=camsrc ! video/x-raw\(memory:GBM\),format=NV12,width=1280,height=720,framerate=30/1,compression=ubwc ! queue ! tee name=split \
    split. ! queue ! qtivcomposer name=mixer sink_1::dimensions="<1920,1080>" ! queue ! video/x-raw\(memory:GBM\),format=NV12,width=1920,height=1080,interlace-mode=progressive,colorimetry=bt601 ! v4l2h264enc capture-io-mode=5 output-io-mode=5 ! h264parse ! queue ! mp4mux ! queue ! filesink location=/opt/video.mp4 \
    split. ! queue ! qtimlvconverter ! queue ! qtimltflite delegate=external external-delegate-path=libQnnTFLiteDelegate.so external-delegate-options="QNNExternalDelegate,backend_type=htp;" model=/opt/posenet_mobilenet_v1.tflite ! queue ! qtimlvpose threshold=51.0 results=2 module=posenet labels=/opt/posenet_mobilenet_v1.labels constants="Posenet,q-offsets=<128.0,128.0,117.0>,q-scales=<0.0784313753247261,0.0784313753247261,1.3875764608383179>;" ! video/x-raw,format=BGRA,width=640,height=360 ! queue ! mixer.Copy to clipboard

To stop the use case, press CTRL + C.

Figure : Pipeline for pose estimation and encode using qtivcomposer
                
                ![](data:image/png;base64,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)

The figure shows the flow of the use case execution:

- Classify scenes from the video stream coming through a camera source.
- Compose the poses and video stream together using qtivcomposer.
- Encode this stream as an H.264 bitstream.
- Multiplex in an MP4 container and storing it as an MP4 file.

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

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70015-50/topic/qtiqmmfsrc.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_gxv_t14_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-pose-estimation-and-encode__ol_hxv_t14_vbc"><br>                                            <li class="li">One stream is sent to the qtivcomposer plugin to<br>                                                retain 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/80-70015-50/topic/qtimlvconverter.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_ixv_t14_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-pose-estimation-and-encode__ul_jxv_t14_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 PoseNet model uses this tensor stream for<br>                                            inferencing.</p><br></li><br><br>                                </ol> |
| **Inferencing** | **Inferencing** |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimltflite.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_kxv_t14_vbc"><br>                                    <li class="li">Loads the PoseNet 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>                                        pose estimation results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvpose](https://docs.qualcomm.com/doc/80-70015-50/topic/qtimlvpose.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_lxv_t14_vbc"><br>                                    <li class="li">Receives the inference tensors from a PoseNet model on its<br>                                        sinkpad.</li><br><br>                                    <li class="li">Converts the tensors into formats such as video or text that<br>                                        the multimedia plugins can process 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 of the pose estimation<br>                                        models. <p class="p">In this use case, qtimlvpose does the<br>                                            following:</p><ol class="ol" type="a" id="single-camera-stream-with-pose-estimation-and-encode__ol_mxv_t14_vbc"><br>                                            <li class="li">Loads the PoseNet submodule.</li><br><br>                                            <li class="li">Produces results as video frames with poses<br>                                                drawn.</li><br><br>                                            <li class="li">Sends them to the sinkpad of the qtivcomposer.</li><br><br>                                        </ol><br></li><br><br>                                </ol> |
|  | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_wjj_x14_vbc"><br>                                    <li class="li">Receives the original video stream and video stream of poses<br>                                        on its sinkpads.</li><br><br>                                    <li class="li">On its sourcepad, produces the GST buffers with the contents<br>                                        composed of video streams from its sinkpads.</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70015-50/topic/qtioverlay.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_oxv_t14_vbc"><br>                                    <li class="li">Receives the multiplexed stream.</li><br><br>                                    <li class="li">Overlays the poses 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/80-70015-50/topic/v4l2h264enc.html) | <ol class="ol" id="single-camera-stream-with-pose-estimation-and-encode__ol_pxv_t14_vbc"><br>                                    <li class="li">Applies parameters to each frame of the video stream that it<br>                                        is 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 additional information corresponding to the bitstream to<br>                                GStreamer buffer meta. |
| mp4mux | Receives the buffers and creates containers format specification<br>                                buffers. |
| **Output** | **Output** |
| Filesink | Stores the resulting stream in a<br>                                    /opt/video.mp4 file. |
| Playback | Pull video.mp4 from the host machine and<br>                                play it on a media player:<br>`scp root@<IP address of<br>                                        target device>:/opt/ <destination<br>                                directory>` |

**Parent Topic:** [TensorFlow Lite use cases](https://docs.qualcomm.com/doc/80-70015-50/topic/tensorflow-lite-use-cases.html)

Last Published: Oct 27, 2025

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