Abnormal Event Detection with Self-guiding Multi-instance Ranking Framework

The detection of abnormal events in surveillance videos with weak supervision is a challenging task, which tries to temporally find abnormal frames using readily accessible video-level labels. In this paper, we propose a self-guiding multi-instance ranking (SMR) framework, which has explored task-specific deep representations and considered the temporal correlations between video clips. Specifically, we apply a clustering algorithm to fine-tune the features extracted by the pre-trained 3D-convolutional-based models. Besides, the clustering module can generate clip-level labels for abnormal videos, and the pseudo-labels are in part used to supervise the training of the multi-instance regression. While implementing the regression module, we compare the effectiveness of various recurrent neural networks, and the results demonstrate the necessity of temporal correlations for weakly supervised video anomaly detection tasks. Experimental results on two standard benchmarks reveal that the SMR framework is comparable to the state-of-the-art approaches, with frame-level AUCs of 81.7% and 92.4% on the UCF-crime and UCSD Ped2 datasets respectively. Additionally, ablation studies and visualization results prove the effectiveness of the component, and our framework can accurately locate abnormal events.