# Pose estimation and encode with TFLite

Source: [https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-pose-estimation-and-encode.html](https://docs.qualcomm.com/doc/80-70014-50/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 a H.264 bitstream.

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

Use the following commands to execute 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 scenes from video stream coming through camera
                    source.
2. Overlay the available poses using overlaylib.
3. Encode this stream as a H.264 bitstream.
4. Multiplex the stream in a MP4 container and store it as a MP4 file.

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

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70014-50/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 qtimetamux plugin to retain<br>                                                the video stream.</li><br><br>                                            <li class="li">The other stream is sent to a ML inferencing<br>                                                pipeline.</li><br><br>                                        </ul><br></li><br><br>                                </ol> |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70014-50/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 model expects<br>                                                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/80-70014-50/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">Executes the inference and produces tensor stream with the<br>                                        pose estimation results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvpose](https://docs.qualcomm.com/doc/80-70014-50/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>                                        can be processed by the multimedia plugins 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/80-70014-50/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 contents of video stream on its<br>                                        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/80-70014-50/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/80-70014-50/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 | Use the following command to pull video.mp4<br>                                from the host machine and play it on a media player<br>                                        application:<br>`scp root@<IP address of target<br>                                        device>:/opt/ <destination<br>                                directory>` |

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

Use the following command to execute 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 camera source.
- Compose the poses and video stream together using qtivcomposer.
- Encode this stream as a H.264 bitstream.
- Multiplex in a MP4 container and storing it as a MP4 file.

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

| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70014-50/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 qtivcomposer plugin to retain<br>                                                the video stream.</li><br><br>                                            <li class="li">The other stream is sent to a ML inferencing<br>                                                pipeline.</li><br><br>                                        </ul><br></li><br><br>                                </ol> |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70014-50/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 model expects<br>                                                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/80-70014-50/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">Executes the inference and produces tensor stream with the<br>                                        pose estimation results on its source pad.</li><br><br>                                </ol> |
| **Postprocessing** | **Postprocessing** |
| [qtimlvpose](https://docs.qualcomm.com/doc/80-70014-50/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>                                        can be processed by the multimedia plugins 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 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 contents<br>                                        composed of video streams from its sinkpads.</li><br><br>                                </ol> |
| [qtioverlay](https://docs.qualcomm.com/doc/80-70014-50/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/80-70014-50/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 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 | Use the following command to pull video.mp4<br>                                from the host machine and play it on a media player<br>                                        application:<br>`scp root@<IP address of target<br>                                        device>:/opt/ <destination<br>                                directory>` |

**Parent Topic:** [TensorFlow Lite use cases](https://docs.qualcomm.com/doc/80-70014-50/topic/tensorflow-lite-use-cases.html)

Last Published: Oct 27, 2025

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