Body Part Segmentation for Occluded Target Tracking
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Solution Overview
Problem
Conventional action recognition systems face difficulties in accurately recognizing the state of monitoring targets, especially when targets overlap, move behind objects, or exit the camera's field of view, leading to challenges in maintaining tracking and assigning correct IDs.
Innovation Solution
The proposed state recognition apparatus employs a camera system with an action recognition processor that captures images, tracks motion, and assigns IDs based on feature extraction and spatiotemporal analysis, using a dictionary for evaluation and re-identification when tracking becomes difficult, ensuring continuous recognition even during partial occlusion or movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video processing is used to detect trajectory of center of gravity for action determination, then action recognition can be performed, but tracking accuracy deteriorates when targets overlap, move behind objects, or exit field of view
Solution Approach 1:
The system segments the monitoring target into multiple body parts (head, torso, limbs) and tracks each part independently. This segmentation allows the system to maintain tracking even when the entire target is occluded, as long as some body parts remain visible. The body part-level tracking provides more robust measurement precision for action recognition while improving overall tracking reliability.
Solution Approach 2:
The system introduces body part trajectories as intermediary elements between the camera and the complete target tracking. By detecting and tracking individual body parts (head, torso, limbs) as intermediaries, the system can reconstruct target motion and maintain reliable tracking even when the complete target is occluded or exits the field of view.
2Device complexity
If conventional video processing tracks the entire target, then tracking is simple, but measurement precision deteriorates when the target is partially occluded or difficult to detect
Solution Approach 1:
The system divides the target into multiple body parts and tracks each separately, improving measurement precision for state recognition. Although this increases processing complexity, the segmented approach provides more reliable data for action determination by analyzing motion patterns of individual body parts even when the complete target is occluded.
3Reliability
If the system re-recognizes the target when detection becomes difficult, then tracking can be maintained, but loss of information occurs during the recognition transition
Solution Approach 1:
The system performs preliminary recognition of body parts and establishes tracking before complete occlusion occurs. By detecting and tracking individual body parts in advance, the system maintains continuous tracking information even when the complete target becomes difficult to detect, preventing information loss during transitions.
Solution Approach 2:
The system changes the recognition parameters from whole-target detection to body part detection. This parameter change allows the system to maintain tracking by detecting smaller, more easily identifiable body parts (head, torso, limbs) even when the complete target is occluded, thereby maintaining tracking continuity without significant information loss.
Data Source
AI summary
A state recognition apparatus includes circuitry configured to: recognize a monitoring target based on a captured image; recognize a motion of each part of the recognized monitoring target; recognize a state of the monitoring target based on the recognized motion of each part; and output a state recognition result indicating the recognized state of the monitoring target.