Multi-Camera Target Tracking Orientation Grouping
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Solution Overview
Problem
Multi-Target Multi-Camera Tracking (MTMCT) faces challenges in associating tracklets across cameras due to differences in target orientation, leading to decreased accuracy and integrity of tracking information, as varying orientations result in distinct appearance features that hinder the association of tracklets corresponding to the same target.
Innovation Solution
A method and device for MTMCT that involves extracting tracklets from multiple cameras, determining the orientation of each tracklet, grouping them based on orientation, performing clustering within each group to aggregate tracklets of the same target, and merging across groups to create a final set of tracklets for accurate tracking information, thereby overcoming the impact of orientation differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If tracklet association is performed based on appearance features alone, then the association process is simple, but the accuracy of tracklet association decreases due to different target orientations causing different appearance features
Solution Approach 1:
The patent segments the tracklet association process into two distinct stages: first grouping tracklets by orientation, then performing feature-based association within each orientation group. This segmentation resolves the contradiction by adding orientation-based classification (increasing process complexity) to enable accurate association across different orientations (improving association accuracy), while keeping each individual stage relatively simple.
2Productivity
If all tracklets are processed together without orientation grouping, then the processing efficiency is high, but the completeness of tracking information decreases due to failed associations between different orientations
Solution Approach 1:
The patent divides the tracklet set into multiple orientation groups before processing, allowing efficient feature-based association within each group while ensuring that tracklets of the same target with different orientations are captured across groups. The subsequent merging step reconstructs complete target trajectories, resolving the contradiction between processing efficiency and tracking completeness.
Solution Approach 2:
The patent adds orientation as an additional dimension for organizing tracklets, transforming the flat processing approach into a multi-dimensional structure (orientation groups × feature space). This enables efficient processing within each orientation slice while capturing complete target information across all orientation dimensions through the merging step.
3Measurement precision
If orientation-based grouping is introduced for tracklet association, then the accuracy and completeness of tracking information improves, but the device complexity increases due to additional grouping and merging operations
Solution Approach 1:
The patent implements orientation-based grouping that segments tracklets into distinct orientation categories, enabling accurate association within each group. The additional complexity of grouping and merging operations is justified by the significant improvement in tracking accuracy and completeness, particularly for targets that change orientation across different camera views.
Data Source
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AI summary
Method and device for target tracking and storage medium are disclosed. The method includes: performing multi-target tracking on an image sequence captured by each of a plurality of cameras, to extract a tracklet for each target appearing in the image sequence, wherein a plurality of tracklets for a plurality of targets are extracted on the basis of a plurality of image sequences; extracting a feature for each tracklet; determining an orientation of each tracklet based on an orientation of the target relative to the camera; dividing the plurality of tracklets into a plurality of groups based on the determined orientations; performing clustering on tracklets in each group based on the extracted features, such that tracklets corresponding to the same target are aggregated into an initial set; performing merging among a plurality of initial sets obtained by performing clustering on the plurality of groups respectively, such that tracklets corresponding to the same target and having different orientations are merged into a final set; and using the tracklets in the final set as tracking information for the corresponding target.