Tracklet Splitting for Multi-Target Tracking Identification Errors
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Solution Overview
Problem
Multi-target tracking in computer vision faces challenges such as occlusion, target overlapping, and illumination changes, leading to inaccurate identification-switch and incorrect trajectories in tracking results.
Innovation Solution
A method for post-processing tracklets in multi-target tracking that involves determining re-identification features, identifying candidate identification switch image patches based on feature similarities, and verifying credible identification-switch events to split tracklets into more accurate trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-target tracking is performed without post-processing, then processing speed is maintained, but identification-switch errors occur and tracking accuracy deteriorates
Solution Approach 1:
The patent segments the tracking process into two distinct stages: real-time tracking (without post-processing) and post-processing refinement. The post-processing module separately handles identification-switch detection and tracklet splitting, allowing the main tracking system to operate efficiently while accuracy is improved in a dedicated refinement stage.
Solution Approach 2:
The patent performs preliminary actions by detecting candidate identification-switch points and verifying them before final tracklet splitting. The verification process checks multiple conditions (occlusion, direction change, feature similarity) in advance to ensure accurate splitting decisions, preventing erroneous corrections.
2Measurement precision
If post-processing with verification is performed, then identification-switch accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies local quality by performing detailed verification only at candidate identification-switch points rather than throughout the entire tracklet. The verification checks (occlusion rate, direction change angle, feature similarity) are localized to specific suspicious points, reducing overall processing time while maintaining accuracy where it matters most.
Solution Approach 2:
The patent maintains continuity by processing tracklets in a continuous pipeline where post-processing operates on completed tracklets without interrupting the main tracking flow. The verification and splitting operations continue seamlessly as the tracking system processes video frames in real-time.
3Reliability
If tracklet splitting is performed without verification, then processing speed is maintained, but false splitting increases and reduces reliability
Solution Approach 1:
The patent implements feedback mechanisms where the verification process uses multiple indicators (occlusion rate, direction change, feature similarity) to feedback on whether a candidate split point is genuine. This multi-factor feedback system reduces false splitting by cross-validating evidence before committing to tracklet division.
Solution Approach 2:
The patent introduces an intermediary verification module that acts as a mediator between candidate identification-switch detection and final tracklet splitting. This intermediary layer evaluates multiple conditions and only permits splitting when verification succeeds, preventing false splits while maintaining system modularity.
Data Source
AI summary
The present disclosure relates to a method, device and storage medium for post-processing in multi-target tracking. According to an embodiment of the present disclosure, the method comprises making attempts to split a tracklet indicative of a trajectory of a single target by performing operations of: determining a re-identification feature set of an image patch sequence by determining a re-identification feature of each image patch in the image patch sequence of the tracklet; determining whether a candidate identification switch image patch is present in the tracklet based on feature similarities of a plurality of re-identification feature pairs in the re-identification feature set; in a case where a determination result is “yes”, verifying whether it is credible that identification-switch has occurred at the candidate identification switch image patch; and in a case where a verification result is “credible”, splitting the tracklet into two tracklets based on the candidate identification switch image patch.