Violence Detection and Recognition from Diverse Video Sources

Abstract

The world is witnessing a vast increase in the crime rate, accompanied by severe losses of lives and property. Surveillance cameras are present everywhere. However, human supervision is becoming inefficient and incapable of early detection of violent acts, due to the diversity and the unexpected scenarios of violence. Accordingly, contributing to automatic violence prevention is becoming exceedingly important. Fulfilling this urgent demand, we turn to Machine Learning to help detect and classify violent events from video streams. In this paper, we propose a framework for detecting violence in video captures and streams, followed by categorizing the violent act in case the video clip is classified as violent. Supervised Learning is applied for both, the binary classification problem and the multi-class violence classification problem. The detection model relies on the usage of 3D Convolutional Neural Networks. The classification model utilizes the pre-trained Inception-v3 model for feature extraction, followed by Gated Recurrent Units (GRUs) for temporal processing. The models are trained on multiple datasets, whose frame-level annotations are available. We meant to use videos from various sources such as surveillance cameras, human recordings, movies, and public websites such as YouTube, to demonstrate the effectiveness of our models on different data sources. Furthermore, Transfer Learning from pre-trained models is applied, where each model trained on one of the datasets, is lightly re-trained on a different one, mostly demonstrating better performance than the original model, in terms of computational resources demands and accuracy.