# Daisy chain detection and pose estimation
Source: [https://docs.qualcomm.com/doc/80-70020-50/topic/daisy-chain-detection-and-pose-detection.html](https://docs.qualcomm.com/doc/80-70020-50/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, runs inferences on AI hardware, and displays the results on the
screen.
For information about the plugins used in the pipeline flow, see [Pipeline flow](https://docs.qualcomm.com/doc/80-70020-50/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
## Sample model and label files
Table : Sample model and label files for gst-ai-daisychain-detection-pose
| Runtime | Model files | Label files |
| :--- | :--- | :--- |
| LiteRT |
Detection: yolox_quantized.tflite
Pose: hrnet_pose_quantized.tflite
|
Detection: yolox.labels
Pose: hrnet_pose.labels
|
## 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.
For model and label files, see [Sample model and label files](https://docs.qualcomm.com/doc/80-70020-50/topic/daisy-chain-detection-and-pose-detection.html#daisy-chain-detection-and-pose-detection__section_uvq_rsg_4dc).
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/80-70020-50/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/80-70020-50/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": "",
"rtsp-ip-port": ""
"detection-model": "",
"detection-labels": "",
"detection-constants": "",
"pose-model": ""
"output-file": ""
}Copy to clipboard
For example, run the application using the custom video
input file, model and label paths, and
constants:
{
"input-file": "/etc/media/video.mp4",
"detection-model": "/etc/models/yolov8_det_quantized.tflite",
"detection-labels": "/etc/labels/yolov8.labels",
"pose-model": "/etc/models/hrnet_pose_quantized.tflite",
"pose-labels": "/etc/labels/hrnet_pose.labels",
"detection-constants": "YOLOx,q-offsets=<38.0, 0.0, 0.0>,q-scales=<3.6124823093414307, 0.003626860911026597, 1.0>;",
"pose-constants": "hrnet,q-offsets=<8.0>,q-scales=<0.0040499246679246426>;"
}Copy to clipboard
4. Run the gst-ai-daisychain-detection-classification
application:
gst-ai-daisychain-detection-pose --config-file=/etc/configs/config-daisychain-detection-pose.jsonCopy to clipboard
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

## 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/80-70020-50/topic/qtiqmmfsrc.html) |
Captures the live stream from camera.
Uses tee to split the stream for inferencing.
|
| File source: filesrc |
Captures the video stream using filesrc, followed by qtdemux, which demultiplexes the stream.
Uses tee to split the stream for inferencing.
|
| RTSP source: rtspsrc |
Captures the RTSP stream using rtspsrc, followed by rtph264depay for video extraction.
Uses tee to split the stream for inferencing.
|
| [qtimetamux](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimetamux.html) | Multiplexes the stream. |
| h264parse | Parses the H.264 video. |
| [v4l2h264dec](https://docs.qualcomm.com/doc/80-70020-50/topic/v4l2h264dec.html) | Decodes the H.264 video stream. |
| [qtivsplit](https://docs.qualcomm.com/doc/80-70020-50/topic/qtivsplit.html) | Crops full frame into smaller frames based on the detected bounding boxes detected (maximum 4). |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvconverter.html) | Used by AI processing stream for preprocessing:
Receives the video stream on its sink pad.
Performs the following preprocessing on the stream data. This preprocessing is done when the model expects floating-point values as input.
Color conversion
Scaling (up or down)
Normalization
Converts the preprocessed video stream to a tensor stream on its source pad.
The tensor stream is used for inferencing in the later stages of the pipeline. |
| [qtimltflite](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimltflite.html) |
After the inference runtime receives the tensor stream on its sink pad, it runs the inference.
Produces a tensor stream with the inference results on its source pad.
|
| [qtimlvdetection](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvdetection.html) | Handles inference results from any object detection model.
Applies a threshold to the chosen number of results.
Loads the YOLOv8 module.
Generates results in the form of video frames with detection labels.
Produces video frames with only bounding boxes that can be cropped.
Forwards the video stream received on its sink pad to Weston.
Weston renders the video stream on a local display.
|
## 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:
input-file: The directory path to the video file.
rtsp-ip-port: The address of the RTSP stream in rtsp://<ip>:<port>/<stream> format.
|
| **Models and labels** |
detection-model: The path to the detection model.
detection-labels: The path to the detection label.
detection-constants: The LiteRT detection model constants.
pose-model: The path to the pose model.
pose-labels: The path to the pose label.
pose-constants: The LiteRT pose model constants.
|
| **Output source** | `output-file`: The directory path to save the output file. The display isn't enabled if this field is empty. |
## Known issue
The application doesn't apply PoseNet within the regions of interest (ROI).
## Related information
- [Daisy chain detection and pose detection using Python](https://docs.qualcomm.com/doc/80-70020-50/topic/daisy-chain-detection-and-pose-detection-using-python.html)
- [Object detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-object-detection.html)
**Parent Topic:** [Run AI/ML sample applications](https://docs.qualcomm.com/doc/80-70020-50/topic/ai-ml-sample-applications.html)
Last Published: Jan 30, 2026
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