  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.
