Improving Surveillance Object Detection with Adaptive Omni-Attention over both Inter-Frame and Intra-Frame Context

Tingting Yu, Chen Chen, Yichao Zhou, Xiyuan Hu; Proceedings of the Asian Conference on Computer Vision (ACCV), 2022, pp. 2697-2712

Surveillance object detection is a challenging and practical sub-branch of object detection. Factors such as lighting variations, smaller objects, and motion blur in video frames affect detection results, but on the other hand, the temporal information and stable background of a surveillance video are major advantages that does not exist in generic object detection. In this paper, we propose an adaptive omni-attention model for surveillance object detection, which effectively and efficiently integrates inter-frame contextual information to improve the detection of low-quality frames and intra-frame attention to suppress false positive detections in the background regions. In addition, the training of the proposed network can converge quickly with less epochs because during multi-frame fusion stage, the pre-trained weights of the single-frame network can be used to update simultaneously in reverse in both single-frame and multi-frame feature maps. The experimental results on the UA-DETRAC and the UAVDT datasets have demonstrated the promising performance of our proposed detector in both accuracy and speed.(Code is available at https://github.com/Yubzsz/Omni-Attention-VOD.)