  Identifying traffic accidents in driving videos is crucial to ensuring the
safety of autonomous driving and driver assistance systems. To address the
potential danger caused by the long-tailed distribution of driving events,
existing traffic accident detection (TAD) methods mainly rely on unsupervised
learning. However, TAD is still challenging due to the rapid movement of
cameras and dynamic scenes in driving scenarios. Existing unsupervised TAD
methods mainly rely on a single pretext task, i.e., an appearance-based or
future object localization task, to detect accidents. However, appearance-based
approaches are easily disturbed by the rapid movement of the camera and changes
in illumination, which significantly reduce the performance of traffic accident
detection. Methods based on future object localization may fail to capture
appearance changes in video frames, making it difficult to detect ego-involved
accidents (e.g., out of control of the ego-vehicle). In this paper, we propose
a novel memory-augmented multi-task collaborative framework (MAMTCF) for
unsupervised traffic accident detection in driving videos. Different from
previous approaches, our method can more accurately detect both ego-involved
and non-ego accidents by simultaneously modeling appearance changes and object
motions in video frames through the collaboration of optical flow
reconstruction and future object localization tasks. Further, we introduce a
memory-augmented motion representation mechanism to fully explore the
interrelation between different types of motion representations and exploit the
high-level features of normal traffic patterns stored in memory to augment
motion representations, thus enlarging the difference from anomalies.
Experimental results on recently published large-scale dataset demonstrate that
our method achieves better performance compared to previous state-of-the-art
approaches.

Computer Vision and Pattern Recognition;Artificial Intelligence