# Daisy chain detection and pose estimation

Source: [https://docs.qualcomm.com/doc/80-70023-50/topic/daisy-chain-detection-and-pose-detection.html](https://docs.qualcomm.com/doc/80-70023-50/topic/daisy-chain-detection-and-pose-detection.html)

The **gst-ai-daisychain-detection-pose** application allows you to perform
        cascaded object detection and pose detection with a camera, file source, or an RTSP stream.
        The use cases involve detecting objects and estimating the body poses of the subject in an
        image or a video.

The following figure show the application workflow, which receives the source,
            postprocesses it, and runs inferences on AI hardware. The results are either displayed
            on the screen, saved as an encoded MP4 file, or streamed over the RTSP server.

For information about the plugins used in the pipeline flow, see [Pipeline flow](https://docs.qualcomm.com/doc/80-70023-50/topic/daisy-chain-detection-and-pose-detection.html#daisy-chain-detection-and-pose-detection__section_pqq_1ny_kbc).

Figure : gst-ai-daisychain-detection-pose pipeline
            
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## Sample model and label files

Table : Sample model and label files for gst-ai-daisychain-detection-pose

| Runtime | Model files | Label files |
| :--- | :--- | :--- |
| LiteRT | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_z1y_2tg_4dc"><br>                                        <li class="li">Detection:<br>                                            <var class="keyword varname">yolox_quantized.tflite</var></li><br><br>                                        <li class="li">Pose:<br>                                            <var class="keyword varname">hrnet_pose_quantized.tflite</var></li><br><br>                                    </ul> | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_t23_vsg_4dc"><br>                                        <li class="li">Detection: <var class="keyword varname">yolox.json</var></li><br><br>                                        <li class="li">Pose: <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_ptd_lyn_xgc"><br>                                                <li class="li"><var class="keyword varname">hrnet_pose.json</var></li><br><br>                                                <li class="li"><var class="keyword varname">hrnet_settings.json</var></li><br><br>                                            </ul><br></li><br><br>                                    </ul> |

## Run the application on the target device

Note: The commands in this section are targeted for the sample
                applications based on QLI GA 1.5 (PPA version 05900 in Ubuntu) or later releases.
                Run the `apt-cache policy gstreamer1.0-qcom-sample-apps` command to
                check your QIM version. If you are using sample applications from older versions,
                run the application with the `--help` option for more
                instructions.

The sample application uses the
                    config-file=/etc/configs/config-daisychain-detection-pose.json
                file to read the input parameters.

To create your own config JSON file, use [config-daisychain-detection-pose.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-daisychain-detection-pose/config-daisychain-detection-pose.json) as a reference.

1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70023-50/topic/download-model-and-label-files.html).
2. Update the config JSON file based on the model, input stream, and other
                    properties. For more information, see [Config JSON field description](https://docs.qualcomm.com/doc/80-70023-50/topic/daisy-chain-detection-and-pose-detection.html#daisy-chain-detection-and-pose-detection__section_mxw_t2r_32c).
    For QCS6490, if `file-path` and
                            `rtsp-ip-port` are *not* present in the
                        configuration file, then the camera input is selected.
3. Use the following format of the
                        config-file=/etc/configs/config-daisychain-detection-pose.json
                        file:

        {
          "input-file": "<input-video-path>",
          "rtsp-ip-port": "<RTSP-IP-port-address>",	
          "detection-model": "<path-to-detection model>",
          "detection-labels": "<path-to-detection-labels>",
          "detection-runtime":"<It can take dsp, gpu or cpu as runtime. Runs detection model on given runtime>",
          "pose-runtime":"<It can take dsp, gpu or cpu as runtime. Runs pose model on given runtime>",
          "output-file": "<output-file-path>"
        }Copy to clipboard

For example, run the application using the custom video
                        input file, model paths, and label
                        paths:

        {
            "input-file": "/etc/media/video.mp4",
            "pose-runtime":"dsp",
            "detection-runtime":"dsp",
            "detection-model": "/etc/models/yolox_quantized.tflite",
            "detection-labels": "/etc/labels/yolox.json",
            "pose-model": "/etc/models/hrnet_pose_quantized.tflite",
            "pose-labels": "/etc/labels/hrnet_pose.json",
            "pose-settings-path":"/etc/labels/hrnet_settings.json"
        }Copy to clipboard
4. Run the gst-ai-daisychain-detection-pose
                        application:

        gst-ai-daisychain-detection-pose --config-file=/etc/configs/config-daisychain-detection-pose.jsonCopy to clipboard

Note: For USB camera input, set the
                            `video-format`, `resolution`, and
                            `framerate` parameters in the configuration file to match
                        the capabilities of the camera. To check the camera capabilities, see [Configure USB camera](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-8/usb.html#configure-usb-camera).

Note: If a drop in performance is observed, you can use
                        YOLOv8 LiteRT model. For YOLOv8 export instructions, see Step 6 in [Prerequisites](https://docs.qualcomm.com/doc/80-70023-50/topic/download-model-and-label-files.html).
5. To display the available help options, run the following command in the SSH
                    shell:

        gst-ai-daisychain-detection-pose -hCopy to clipboard
6. To stop the use case, use CTRL +
                    C.

## Expected output

Figure : Expected output for gst-ai-daisychain-detection-pose application
                
                ![](data:image/png;base64,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)

## Pipeline flow

The following table lists the plugins used in the daisy chain detection and
                    pose estimation pipeline:| Plugin | Description |
| --- | --- |
| Camera source:[qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70023-50/topic/qtiqmmfsrc.html) | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_zyl_gj1_mcc"><br>                                    <li class="li">Captures the live stream from camera.</li><br><br>                                    <li class="li">Uses tee to split the stream for inferencing.</li><br><br>                                </ul> |
| File source: filesrc | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_z1z_x4f_w1c"><br>                                    <li class="li">Captures the video stream using filesrc, followed by<br>                                        qtdemux, which demultiplexes the stream.</li><br><br>                                    <li class="li">Uses tee to split the stream for inferencing.</li><br><br>                                </ul> |
| RTSP source: rtspsrc | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_vsj_2r4_tbc"><br>                                    <li class="li">Captures the RTSP stream using rtspsrc, followed by<br>                                        rtph264depay for video extraction.</li><br><br>                                    <li class="li">Uses tee to split the stream for inferencing.</li><br><br>                                </ul> |
| USB camera source: v4l2src | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_ptd_t21_4gc"><br>                                    <li class="li">Captures the live stream from USB camera.</li><br><br>                                    <li class="li">Uses tee to split the stream for inferencing.</li><br><br>                                </ul> |
| [qtimetamux](https://docs.qualcomm.com/doc/80-70023-50/topic/qtimetamux.html) | Multiplexes the stream. |
| h264parse | Parses the H.264 video. |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70023-50/topic/v4l2h264dec.html) | Decodes the H.264 video stream. |
| [qtivsplit](https://docs.qualcomm.com/doc/80-70023-50/topic/qtivsplit.html) | Crops full frame into smaller frames based on the detected<br>                                bounding boxes detected (maximum 4). |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70023-50/topic/qtimlvconverter.html) | Used by AI processing stream for preprocessing:<ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_j34_ddg_q1c"><br>                                    <li class="li">Receives the video stream on its sink pad.</li><br><br>                                    <li class="li">Performs the following preprocessing on the stream data.<br>                                        This preprocessing is done when the model expects<br>                                        floating-point values as input.<ol class="ol" type="a" id="daisy-chain-detection-and-pose-detection__ol_m5z_cpr_lbc"><br>                                            <li class="li">Color conversion</li><br><br>                                            <li class="li">Scaling (up or down)</li><br><br>                                            <li class="li">Normalization</li><br><br>                                        </ol><br></li><br><br>                                    <li class="li">Converts the preprocessed video stream to a tensor stream on<br>                                        its source pad. </li><br><br>                                </ol><br><br>The tensor stream is used for inferencing in the later<br>                                    stages of the pipeline. |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70023-50/topic/qtimltflite.html) | <ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_l2x_zjq_nbc"><br>                                    <li class="li">After the inference runtime receives the tensor stream on<br>                                        its sink pad, it runs the inference.</li><br><br>                                    <li class="li">Produces a tensor stream with the inference results on its<br>                                        source pad.</li><br><br>                                </ol> |
| qtimlpostprocess for detection model | Handles inference results from any object detection model.<ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_ol3_dky_kbc"><br>                                    <li class="li">Applies a threshold to the chosen number of results.</li><br><br>                                    <li class="li">Loads the YOLOv8 module. </li><br><br>                                    <li class="li">Generates results in the form of video frames with detection<br>                                        labels.</li><br><br>                                    <li class="li">Produces video frames with only bounding boxes that can be<br>                                        cropped. </li><br><br>                                </ol> |
| qtimlpostprocess for pose estimation | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_jk5_b1s_lbc"><br>                                    <li class="li">Applies a threshold to the chosen number of results. </li><br><br>                                    <li class="li">Loads corresponding modules for various pose detection<br>                                        models. <p class="p">In the use case described in this section,<br>                                            qtimlpostprocess does the following:</p><ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_w5v_2qr_lbc"><br>                                            <li class="li">Loads the HRNet module.</li><br><br>                                            <li class="li">Produces results in the form of video frames with<br>                                                drawn poses. </li><br><br>                                            <li class="li">Sends the results to the sink pad of qtivcomposer<br>                                                for further processing or display.</li><br><br>                                        </ol><br></li><br><br>                                </ul> |
| [qtivcomposer](https://docs.qualcomm.com/doc/80-70023-50/topic/qtivcomposer.html) | <ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_y1d_hqr_lbc"><br>                                    <li class="li">Composes frames by combining the contents from its sink<br>                                        pads.</li><br><br>                                    <li class="li">Pushes the GStreamer buffers containing the composed frames<br>                                        onto its source pad.</li><br><br>                                </ol> |
| [Waylandsink](https://docs.qualcomm.com/doc/80-70023-50/topic/waylandsink.html) | <ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_hgj_kqr_lbc"><br>                                    <li class="li">Forwards the video stream received on its sink pad to<br>                                        Weston.</li><br><br>                                    <li class="li">Weston renders the video stream on a local display.</li><br><br>                                </ol> |
| filesink | Receives the video stream on sink pad and saves it as an<br>                                H.264-encoded MP4 file. |
| qtirtspbin | <ol class="ol" id="daisy-chain-detection-and-pose-detection__ol_hjm_3f1_4gc"><br>                                    <li class="li">Serves as a network sink.</li><br><br>                                    <li class="li">Transmits UDP packets to the network.</li><br><br>                                </ol> |

## Config JSON field description

The different parameters available to configure the JSON file and run the use case
                are as follows:

Table : Field description–config_daisychain_detection_pose.json file

| Field | Values/description |
| :--- | :--- |
| **Input source** | Use one of the following input sources:<br><ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_eqk_kfr_32c"><br>                                    <li class="li"><code class="ph codeph">input-file</code>: The directory path to the video<br>                                        file.</li><br><br>                                    <li class="li"><code class="ph codeph">rtsp-ip-port</code>: The address of the RTSP<br>                                        stream in<br>                                                <u class="ph u"><var class="keyword varname">rtsp://&lt;ip&gt;:&lt;port&gt;/&lt;stream&gt;</var></u><br>                                        format.</li><br><br>                                    <li class="li"><code class="ph codeph">enable-usb-camera</code>: Set to TRUE or<br>                                        FALSE.</li><br><br>                                </ul> |
| **Models and labels** | <ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_c31_nfr_32c"><br>                                    <li class="li"><code class="ph codeph">detection-model</code>: The path to the detection<br>                                        model.</li><br><br>                                    <li class="li"><code class="ph codeph">detection-labels</code>: The path to the detection<br>                                        label.</li><br><br>                                    <li class="li"><code class="ph codeph">pose-model</code>: The path to the pose<br>                                        model.</li><br><br>                                    <li class="li"><code class="ph codeph">pose-labels</code>: The path to the pose<br>                                        label.</li><br><br>                                </ul> |
| **Output source** | `output-file`: The directory path to save the<br>                                    output file. The display isn't enabled if this field is<br>                                    empty. |
| **USB camera video-format and resolution** | Use one of the following video-format<ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_ss2_wg1_4gc"><br>                                        <li class="li"><code class="ph codeph">nv12</code></li><br><br>                                        <li class="li"><code class="ph codeph">yuy2</code></li><br><br>                                        <li class="li"><code class="ph codeph">mjpeg</code></li><br><br>                                    </ul><br><br><br>                                <br>Use one of the following resolution fields:<ul class="ul" id="daisy-chain-detection-and-pose-detection__ul_ln3_ch1_4gc"><br>                                        <li class="li"><code class="ph codeph">width</code>: Input USB camera source<br>                                            resolution width.</li><br><br>                                        <li class="li"><code class="ph codeph">height</code>: Input USB camera source<br>                                            resolution width.</li><br><br>                                        <li class="li"><code class="ph codeph">framerate</code>: Input USB camera source<br>                                            framerate.</li><br><br>                                    </ul> |
| **detection-runtime and classification-runtime** | Takes CPU, GPU, and DSP as input and inferences respective use<br>                                case model in a particular runtime. |

## Known issue

Lag is observed with camera source on QCS6490 with both YOLOX and YOLOv8 models.

## Related information

- [Daisy chain detection and pose detection using Python](https://docs.qualcomm.com/doc/80-70023-50/topic/daisy-chain-detection-and-pose-detection-using-python.html)
- [Object detection](https://docs.qualcomm.com/doc/80-70023-50/topic/gst-ai-object-detection.html)

**Parent Topic:** [Run AI/ML sample applications](https://docs.qualcomm.com/doc/80-70023-50/topic/ai-ml-sample-applications.html)

Last Published: Mar 27, 2026

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