Custom Object Detection via Multi-Camera Self-Supervised Learning

• URL: http://arxiv.org/abs/2102.03442v1
• Date: Fri, 5 Feb 2021 23:11:14 GMT
• Title: Custom Object Detection via Multi-Camera Self-Supervised Learning
• Abstract summary: MCSSL is a self-supervised learning approach for building custom object detection models in multi-camera networks. Our evaluation shows that compared with legacy selftraining methods, MCSSL improves average mAP by 5.44% and 6.76% on WildTrack and CityFlow dataset.
• Score: 15.286868970188223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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