Global Context Network Based Detection of Abnormal Video Behaviour
A weakly-supervised video anomalous behaviour detection method based on global context net-work is proposed to address the problem that long-distance temporal feature relationships in video informa-tion are easily ignored.In order to enhance the global understanding of the visual scene and improve the accuracy of anomaly detection,the temporal feature extraction module is improved by calculating only one global attention matrix independent of the query location and sharing it for all the query locations,which effectively reduces the amount of network computation and the number of parameters.Meanwhile,net-work module optimisation is carried out to speed up the calculation speed.The experimental results show that the video anomalous behaviour detection algorithm based on global context network can effectively im-prove the accuracy of anomaly detection with a lighter network and higher computing efficiency.
video anomalous behaviour detectionweak supervisiontemporal featuresglobal attention matrix
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