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Deep Trajectory Post-Processing and Position Projection for Single & Multiple Camera Multiple Object Tracking
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DOI：10.1007/s11263-021-01527-y.png)
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Abstract
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Multiple Object Tracking (MOT) has attracted increasing interests in recent years, which plays a significant role in video analysis. MOT aims to track the specific targets as whole trajectories and locate the positions of the trajectory at different times. These trajectories are usually applied in Action Recognition, Anomaly Detection, Crowd Analysis and Multiple-Camera Tracking, etc. However, existing methods are still a challenge in complex scene. Generating false (impure, incomplete) tracklets directly affects the performance of subsequent tasks. Therefore, we propose a novel architecture, Siamese Bi-directional GRU, to construct Cleaving Network and Re-connection Network as trajectory post-processing. Cleaving Network is able to split the impure tracklets as several pure sub-tracklets, and Re-connection Network aims to re-connect the tracklets which belong to same person as whole trajectory. In addition, our methods are extended to Multiple-Camera Tracking, however, current methods rarely consider the spatial-temporal constraint, which increases redundant trajectory matching. Therefore, we present Position Projection Network (PPN) to convert trajectory position from local camera-coordinate to global world-coodrinate, which provides adequate and accurate temporal-spatial information for trajectory association. The proposed technique is evaluated over two widely used datasets MOT16 and Duke-MTMCT, and experiments demonstrate its superior effectiveness as compared with the state-of-the-arts.
Keywords:
Trajectory post-processing
Position projection
Multiple object tracking
Multiple-camera multiple object tracking
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