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Custom Object Detection via Multi-Camera Self-Supervised Learning

2021-02-05 23:11:14
Yan Lu, Yuanchao Shu

Abstract

This paper proposes MCSSL, a self-supervised learning approach for building custom object detection models in multi-camera networks. MCSSL associates bounding boxes between cameras with overlapping fields of view by leveraging epipolar geometry and state-of-the-art tracking and reID algorithms, and prudently generates two sets of pseudo-labels to fine-tune backbone and detection networks respectively in an object detection model. To train effectively on pseudo-labels,a powerful reID-like pretext task with consistency loss is constructed for model customization. Our evaluation shows that compared with legacy selftraining methods, MCSSL improves average mAP by 5.44% and 6.76% on WildTrack and CityFlow dataset, respectively.

Abstract (translated)

URL

https://arxiv.org/abs/2102.03442

PDF

https://arxiv.org/pdf/2102.03442.pdf


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