  Traffic videos inherently differ from generic videos in their stationary
camera setup, thus providing a strong motion prior where objects often move in
a specific direction over a short time interval. Existing works predominantly
employ generic video object detection framework for traffic video object
detection, which yield certain advantages such as broad applicability and
robustness to diverse scenarios. However, they fail to harness the strength of
motion prior to enhance detection accuracy. In this work, we propose two
innovative methods to exploit the motion prior and boost the performance of
both fully-supervised and semi-supervised traffic video object detection.
Firstly, we introduce a new self-attention module that leverages the motion
prior to guide temporal information integration in the fully-supervised
setting. Secondly, we utilise the motion prior to develop a pseudo-labelling
mechanism to eliminate noisy pseudo labels for the semi-supervised setting.
Both of our motion-prior-centred methods consistently demonstrates superior
performance, outperforming existing state-of-the-art approaches by a margin of
2% in terms of mAP.
