Abstract
Video anomaly detection aims to recognise and analyse the video sequences to classify the normal and abnormal frames. This technology can efficiently reduce the human labour to discover the anomalies in surveillance systems and is widely applied in financial, public security and transport sectors. However, video anomaly detection performance is often degraded by the dataset quality, especially for small objects in video sequences. Besides, the computational cost of the classification model would be required as low as possible. In this paper, we proposed information fusion with a joint model which contains motion estimation, object detection and adversarial learning to detect anomalies in two video datasets:UCSD PED1 and PED2. Experimental results confirm the proposed method outperforms the state-of-the-art methods with the additional advantages in reduced computation cost.
