  Video anomaly detection aims to discover abnormal events in videos, and the
principal objects are target objects such as people and vehicles. Each target
in the video data has rich spatio-temporal context information. Most existing
methods only focus on the temporal context, ignoring the role of the spatial
context in anomaly detection. The spatial context information represents the
relationship between the detection target and surrounding targets. Anomaly
detection makes a lot of sense. To this end, a video anomaly detection
algorithm based on target spatio-temporal context fusion is proposed. Firstly,
the target in the video frame is extracted through the target detection network
to reduce background interference. Then the optical flow map of two adjacent
frames is calculated. Motion features are used multiple targets in the video
frame to construct spatial context simultaneously, re-encoding the target
appearance and motion features, and finally reconstructing the above features
through the spatio-temporal dual-stream network, and using the reconstruction
error to represent the abnormal score. The algorithm achieves frame-level AUCs
of 98.5% and 86.3% on the UCSDped2 and Avenue datasets, respectively. On the
UCSDped2 dataset, the spatio-temporal dual-stream network improves frames by
5.1% and 0.3%, respectively, compared to the temporal and spatial stream
networks. After using spatial context encoding, the frame-level AUC is enhanced
by 1%, which verifies the method's effectiveness.
