Contrastive loss based training strategy for unsupervised multi-object tracking

Abstract

The present invention relates to unsupervised tracking technology, specifically an unsupervised tracking model training strategy based on contrastive loss. The method comprises: S1: forming a constrained SSCI module using the relation between objects within a video frame and between adjacent video frames; S2: setting features of different objects in each frame as negative samples, and similar adjacent frame objects as positive sample pairs, constructing contrastive loss; S3: constraining embedded features (E_t) by variable loss based on self-supervised contrastive loss. This invention provides a contrastive loss-based training strategy for unsupervised multi-object tracking, leveraging the prior that objects in a frame must be different to enhance object similarity, and using self-supervised learning to match similar objects in short-interval frames as positive samples to boost cross-frame feature expression. Finally, it further improves cross-frame feature expression by ensuring consistent forward and reverse matching.

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