Anomalous event detection in surveillance videos is a challenging and
practical research problem among image and video processing community. Compared
to the frame-level annotations of anomalous events, obtaining video-level
annotations is quite fast and cheap though such high-level labels may contain
significant noise. More specifically, an anomalous labeled video may actually
contain anomaly only in a short duration while the rest of the video frames may
be normal. In the current work, we propose a weakly supervised anomaly
detection framework based on deep neural networks which is trained in a
self-reasoning fashion using only video-level labels. To carry out the
self-reasoning based training, we generate pseudo labels by using binary
clustering of spatio-temporal video features which helps in mitigating the
noise present in the labels of anomalous videos. Our proposed formulation
encourages both the main network and the clustering to complement each other in
achieving the goal of more accurate anomaly detection. The proposed framework
has been evaluated on publicly available real-world anomaly detection datasets
including UCF-crime, ShanghaiTech and UCSD Ped2. The experiments demonstrate
superiority of our proposed framework over the current state-of-the-art
methods.

本研究提出了一种基于深度神经网络的弱监督异常检测框架，采用视频级别的标签，通过使用时空视频特征的二元聚类来产生伪标签，从而减少异常视频标签中存在的噪声，实现更准确的异常检测。