Real-time body skeleton detection tracking 17 keypoints per person for safety monitoring, movement analysis, fall detection, and gesture-based interaction.
Visylix Pose Estimation is a real-time body skeleton detection system that tracks 17 keypoints per person to unlock safety monitoring, movement analysis, and gesture-based interaction across video infrastructure. The technology employs a top-down multi-person pipeline that first identifies individuals in a frame, then maps 17 anatomical landmarks covering the head, shoulders, elbows, wrists, hips, knees, and ankles. The resulting skeleton data feeds into downstream applications for fall detection, ergonomic assessment, gesture recognition, and activity classification. Temporal smoothing and occlusion-aware processing ensure stability during fast movement and partial visibility.
Core capabilities of the Pose Estimation model.
Full-body skeleton mapping per person in every frame covering head, shoulders, elbows, wrists, hips, knees, and ankles.
Sub-30ms inference per person on edge GPUs for live skeleton overlay and immediate alert triggering.
Identifies sudden orientation and velocity changes in skeleton patterns; triggers instant notifications to security and medical staff.
Detects configurable upper-body gestures like hand-raise, wave, and pointing for touchless interaction and emergency signaling.
Computes joint angles against established ergonomic standards to identify repetitive-strain risks and unsafe postures in real time.
Simultaneously tracks skeletons for 50+ people in a single frame while maintaining individual identity and temporal consistency.
Classifies activities like walking, running, sitting, bending, and climbing using temporal skeleton sequences for behavioral analysis.
Maintains skeleton tracking accuracy during partial occlusion using predictive joint positioning and temporal interpolation.
Real-world applications for Pose Estimation.
Monitors worker posture in factories and warehouses to detect unsafe lifting techniques, prolonged awkward positions, and fall incidents.
Captures athlete movement during training and competition; analyzes stride length, joint angles, body symmetry, and form optimization.
Tracks patient movement during physical therapy to quantify range of motion, gait asymmetry, and exercise form compliance.
Classifies activities like running, loitering, and collapse in surveillance feeds with contextual alerting for abnormal behavioral sequences.
Powers gesture-controlled kiosks, digital signage, and immersive experiences in museums, retail, and entertainment venues.
Performance and deployment details.
Add Pose Estimation to your video pipeline in minutes.
Assign the model to specific cameras with zone definitions and sensitivity settings through the web UI or API.
The model processes video frames in real time, generating structured detection events with bounding boxes and metadata.
Receive instant alerts via webhooks, trigger automated workflows, or query detections through the REST API.
See how Pose Estimation is applied across different sectors.
Explore other computer vision capabilities.
Talk to our team to see this model in action on your video feeds.