Multi-View Domain Adaptive Object Detection on Camera Networks
Yan Lu, Zhun Zhong, Yuanchao Shu
Abstract
In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods.
BibTeX
@article{Lu_Zhong_Shu_2023, title={Multi-View Domain Adaptive Object Detection on Camera Networks}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26077}, DOI={10.1609/aaai.v37i7.26077}, abstractNote={In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging context, existing methods including adversarial training and self-training fall short due to multi-domain data shift and the lack of source data. To tackle this problem, we propose a novel training framework consisting of two stages. First, we pre-train the backbone using self-supervised learning, in which a multi-view association is developed to construct an effective pretext task. Second, we fine-tune the detection head using robust self-training, where a tracking-based single-view augmentation is introduced to achieve weak-hard consistency learning. By doing so, an object detection model can take advantage of informative samples generated by multi-view association and single-view augmentation to learn discriminative backbones as well as robust detection classifiers. Experiments on two real-world multi-camera datasets demonstrate significant advantages of our approach over the state-of-the-art domain adaptive object detection methods.}, number={7}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lu, Yan and Zhong, Zhun and Shu, Yuanchao}, year={2023}, month={Jun.}, pages={8966-8974} }