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Aggregate Tracklet Appearance Features for Multi-Object Tracking
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DOI：10.1109/LSP.2019.2940922.png)
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Abstract
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Multi-object tracking (MOT) has wide applications in the fields of video analysis and signal processing. A major challenge in MOT is how to associate the noisy detections into long and continuous trajectories. In this letter, we address the association problem at the tracklet-level, and mainly focus on the appearance representation designed for tracklets. A multitask convolutional neural network is proposed to learn the discriminative features and spatial-temporal attentions jointly. In particular, we decompose an object in a static image with spatial attentions, and then aggregate multiple features in a tracklet based on the temporal attentions. Appearance misalignment that caused by occlusion and inaccurate bounding is then mitigated by multi-feature aggregation. Experimental results on two challenging MOT benchmarks have demonstrated the effectiveness of the proposed method and shown significant improvement on the quality of tracking identities.
Keywords:
Target tracking
Trajectory
Feature extraction
Aggregates
Training
Benchmark testing
Multi-object tracking
tracklet association
appearance model
spatial-temporal attention
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