Automatic Detection of Violent Incidents from Video Footage of CCTV Cameras

In the current era, the majority of public places such as supermarket, public garden, malls, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 ? 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. We present an e?cient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. Experimental results on challenging datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level in anomaly detection task.