Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Abstract The use of neural networks in a range of academic and scientific pursuits has introduced a great interest in modeling human behavior and activity patterns to recognize particular events. Various methods have so far been proposed for building expert vision systems to understand the scene and draw true semantic inferences from the observed dynamics. However, classifying abnormal or unusual activities in real-time video sequences is still challenging, as the details in video sequences have a time continuity constraint. A cost-effective approach is still demanding and so this work presents an advanced three-dimensional convolutional network (A3DConvNet) for detecting abnormal behavior of persons by analyzing their actions. The network proposed is 15 layers deep that uses 18 convolutional operations to effectively analyze the video contents and produces spatiotemporal features. The integrated dense layer uses these features for the efficient learning process and the softmax layer is used as the output layer for labeling the sequences. Additionally, we have created a dataset that carries video clips to represent abnormal behaviors of humans in megastores/shops, which is a consequent contribution of this paper. The dataset includes five complicated activities in the shops/megastores: normal, shoplifting, drinking, eating, and damaging. By analyzing human actions, the proposed algorithm produces an alert if anything like abnormalities is found. The extensive experiments performed on the synthesized dataset demonstrate the effectiveness of our method, with achieved accuracy of up to 90.90%.