Malicious Activity Detection In Safe City Environment

Safe cities initiative promises the eradication of crimes specially the volume crimes which constitute fighting, shooting, and vandalism. This paper focuses on the detection of real-world volume crimes in a video using spatio-temporal convolutional neural network. Firstly, a benchmark dataset has been annotated on the fine-grained level to ensure reliable training. Secondly, a comparison is made between two state of the art networks recently used for spatio temporal analysis, over the developed dataset. Also, detailed analysis has been performed for detection and classification over the two networks. From the comparison it is concluded that the performance of C3D model is better than ResNet3D for binary classification. Whereas, ResNet3D outperforms C3d incase of multi-class classification.