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6.4.8 Human Pose Detection

Last Version: 12/09/2025

YOLOv8-Pose Model Overview​

YOLOv8-Pose is a Pose Estimation Model from Ultralytics' YOLOv8 series. It detects human keypoints (like head, shoulders, knees) and builds on YOLOv8's architecture for fast, accurate 2D pose estimation. Typical applications include:

  • Security surveillance
  • Sports analytics
  • Human-computer interaction
  • Robotics

This guide shows how to:

  1. Run YOLOv8-Pose inference on images or video streams using SpacemiT hardware
  2. Publish detection results (bounding boxes + human keypoints) via ROS 2

Environment Setup​

Install Dependencies​

sudo apt install python3-opencv ros-humble-cv-bridge ros-humble-camera-info-manager \
ros-humble-image-transport python3-spacemit-ort

Source ROS2 Environment​

source /opt/bros/humble/setup.bash

Check Available Models​

List available pose estimation models:

ros2 launch br_perception infer_info.launch.py | grep 'pose'

Example output:

[list-1]   - config/pose/yolov8_pose.yaml

For subsequent inference tasks, set the config_path parameter to the corresponding .yaml file path to use that specific pose estimation model.

Human Keypoint Definitions​

The model detects these keypoints as:

Keypoint indices and names:

0. Nose
1. Left eye
2. Right eye
3. Left ear
4. Right ear
5. Left shoulder
6. Right shoulder
7. Left elbow
8. Right elbow
9. Left wrist
10. Right wrist
11. Left hip
12. Right hip
13. Left knee
14. Right knee
15. Left ankle
16. Right ankle

Image Inference​

Prepare a Test Image

cp /opt/bros/humble/share/jobot_infer_py/data/detection/test.jpg .

Run Inference and Save Results Locally​

ros2 launch br_perception infer_img.launch.py \
  config_path:='config/pose/yolov8_pose.yaml' \
  img_path:='./test.jpg'

Results are saved as pose_result.jpg in the current directory:

Terminal outputs detailed detection information:

bianbu@bianbu:~$ ros2 launch br_perception infer_img.launch.py   config_path:='config/pose/yolov8_pose.yaml'   img_path:='./test.jpg'
[INFO] [launch]: All log files can be found below /home/bianbu/.ros/log/2025-07-29-11-38-09-728349-bianbu-13624
[INFO] [launch]: Default logging verbosity is set to INFO
[INFO] [infer_img_node-1]: process started with pid [13631]
[infer_img_node-1] All model files already exist and do not need to be downloaded
[infer_img_node-1] all time cost:0.11103701591491699
[infer_img_node-1]
[infer_img_node-1] Person 0, [xmin:0, ymin:203, width:87, height:151], conf 0.42
[infer_img_node-1]   nose: (49.0, 131.0), vis=1.32
[infer_img_node-1]   left_eye: (55.0, 131.0), vis=1.32
[infer_img_node-1]   right_eye: (49.0, 123.0), vis=1.32
[infer_img_node-1]   left_ear: (63.0, 131.0), vis=1.32
[infer_img_node-1]   right_ear: (49.0, 131.0), vis=0.00
[infer_img_node-1]   left_shoulder: (63.0, 150.0), vis=1.32
[infer_img_node-1]   right_shoulder: (41.0, 150.0), vis=1.32
[infer_img_node-1]   left_elbow: (55.0, 191.0), vis=1.32
[infer_img_node-1]   right_elbow: (16.0, 174.0), vis=1.32
[infer_img_node-1]   left_wrist: (16.0, 183.0), vis=1.32
[infer_img_node-1]   right_wrist: (16.0, 174.0), vis=1.32
[infer_img_node-1]   left_hip: (63.0, 230.0), vis=1.32
[infer_img_node-1]   right_hip: (41.0, 230.0), vis=1.32
[infer_img_node-1]   left_knee: (63.0, 288.0), vis=1.32
[infer_img_node-1]   right_knee: (41.0, 288.0), vis=1.32
[infer_img_node-1]   left_ankle: (80.0, 335.0), vis=1.32
[infer_img_node-1]   right_ankle: (49.0, 335.0), vis=1.32
[infer_img_node-1]
[infer_img_node-1] Person 1, [xmin:1, ymin:112, width:39, height:238], conf 0.85

Web-Based Visualization​

Terminal 1 - Start inference node:

ros2 launch br_perception infer_img.launch.py \
  config_path:='config/pose/yolov8_pose.yaml' \
  img_path:='./test.jpg' \
  publish_result_img:=true \
  result_img_topic:='result_img' \
  result_topic:='/inference_result'

Terminal 2 - Launch web visualization:

ros2 launch br_visualization websocket_cpp.launch.py image_topic:='/result_img'

The terminal will print the message as:

...
Please visit in your browser: http://<IP>:8080
...

Open a browser and visit the URL shown in the terminal (e.g., http://<IP>:8080) to view results:

Subscribe to Results​

To view the inference results by

ros2 topic echo /inference_result

Or use this Python script to subscribe to inference results:

import rclpy
from rclpy.node import Node
from std_msgs.msg import Header
from jobot_ai_msgs.msg import DetectionPoseResultArray

class PoseSubscriber(Node):
    def __init__(self):
        super().__init__('pose_subscriber')
        self.subscription = self.create_subscription(
            DetectionPoseResultArray,
            '/inference_result',  # Topic name, can be changed
            self.listener_callback,
            10  # QoS
        )

    def listener_callback(self, msg):
        self.get_logger().info(f"Received pose results: {len(msg.results)} persons")

        for det_id, res in enumerate(msg.results):
            self.get_logger().info(
                f"\nPerson {det_id}, [xmin:{res.x_min}, ymin:{res.y_min}, "
                f"width:{res.width}, height:{res.height}], conf {res.conf:.2f}"
            )

            for i in range(len(res.keypoint_ids)):
                name = f"kp_{res.keypoint_ids[i]}"
                x = res.keypoint_xs[i]
                y = res.keypoint_ys[i]
                conf = res.keypoint_confs[i]
                self.get_logger().info(f"  {name}: ({x:.1f}, {y:.1f}), vis={conf:.2f}")

def main(args=None):
    rclpy.init(args=args)
    node = PoseSubscriber()
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

Parameters Description​

infer_img.launch.py Parameters

ParameterDescriptionDefault
config_pathPath to the model configuration file used for inferenceconfig/detection/yolov6.yaml
img_pathPath to the image file to run inference ondata/detection/test.jpg
publish_result_imgWhether to publish the detection result as an image messagefalse
result_img_topicTopic name for rendered image output (only valid if publish_result_img = true)/result_img
result_topicTopic name for the inference result message/inference_result

Video Stream Inference​

Start USB Camera​

ros2 launch br_sensors usb_cam.launch.py video_device:="/dev/video20"

Run Inference & Publish results​

Terminal 1 - Start inference:

ros2 launch br_perception infer_video.launch.py \
  config_path:='config/pose/yolov8_pose.yaml' \
  sub_image_topic:='/image_raw' \
  publish_result_img:=true \
  result_topic:='/inference_result'

Terminal 2 - Launch web visualization:

ros2 launch br_visualization websocket_cpp.launch.py image_topic:='/result_img'

The terminal will print the message as:

...
Please visit in your browser: http://<IP>:8080
...

Open a browser and visit the URL shown in the terminal (e.g., http://<IP>:8080) to view results.

Data-Only Inference (No Visualization)

If you only want to obtain the model inference results, run the following command:

ros2 launch br_perception infer_video.launch.py \
  config_path:='config/pose/yolov8_pose.yaml' \
  sub_image_topic:='/image_raw' \
  publish_result_img:=false \
  result_topic:='/inference_result'

Subscribe to Results​

To view the inference results by

ros2 topic echo /inference_result

Or use this Python script to subscribe to inference results:

import rclpy
from rclpy.node import Node
from std_msgs.msg import Header
from jobot_ai_msgs.msg import DetectionPoseResultArray

class PoseSubscriber(Node):
    def __init__(self):
        super().__init__('pose_subscriber')
        self.subscription = self.create_subscription(
            DetectionPoseResultArray,
            '/inference_result',  # Topic name, can be changed
            self.listener_callback,
            10  # QoS
        )

    def listener_callback(self, msg):
        self.get_logger().info(f"Received pose results: {len(msg.results)} persons")

        for det_id, res in enumerate(msg.results):
            self.get_logger().info(
                f"\nPerson {det_id}, [xmin:{res.x_min}, ymin:{res.y_min}, "
                f"width:{res.width}, height:{res.height}], conf {res.conf:.2f}"
            )

            for i in range(len(res.keypoint_ids)):
                name = f"kp_{res.keypoint_ids[i]}"
                x = res.keypoint_xs[i]
                y = res.keypoint_ys[i]
                conf = res.keypoint_confs[i]
                self.get_logger().info(f"  {name}: ({x:.1f}, {y:.1f}), vis={conf:.2f}")

def main(args=None):
    rclpy.init(args=args)
    node = PoseSubscriber()
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()

if __name__ == '__main__':
    main()

Parameters Descriptions​

infer_video.launch.py Parameters

Parameter NameDescriptionDefault Value
config_pathPath to the model configuration file for inferenceconfig/detection/yolov6.yaml
sub_image_topicImage topic to subscribe to/image_raw
publish_result_imgWhether to publish the rendered inference imagefalse
result_img_topicImage topic to publish (only when publish_result_img=true)/result_img
result_topicInference-result topic to publish/inference_result