Crowd abnormality detection in video sequences using supervised convolutional neural network

In this paper, a Convolutional Neural Network (CNN) based crowd abnormality detection model in video sequences is proposed. The model has two convolution layers, two Fully Connected (FC) layers in which 1st FC layer uses Rectified Linear Unit (ReLU) and the 2nd uses sigmoid function as activation functions. Both convolution layers consist of a convolution operator followed by ReLU and the max-pooling layer. Intermediate convolutional layers produce features that are used to detect the abnormality in the video frame. The performance of the proposed model has been evaluated based on three parameters – Receiver Operator Characteristic (ROC) curve, Area Under the Curve (AUC), and Equal Error Rate (EER). Three scientific datasets have been used, consisting of several video sequences with various normal and abnormal activities. Experimental results show that the proposed CNN model performs better for all datasets compared with other similar methods in literature and achieves a maximum of near 100% sensitivity through the ROC curve for one dataset. Further, the average AUC value for the UCSD dataset (Ped1 and Ped2) is close to 98% and 95% for the Avenue dataset. The average EER for the UCSD dataset (Ped1 and Ped2) is near 10% and 11.5% for the Avenue dataset. Moreover, the model has also been evaluated for a couple of random YouTube videos of abnormal behavior, and it gives satisfactory results.