Crime Scene Surveillance with Deep Learning

Crime Scene Surveillance with Deep Learning

Digvijay Patil, Yash Parande, Shivam Pandagale, Om Soni, Parth Mule, Sanskruti Morey

Computer Engineering VIT Pune India

Abstract — The ability to automatically detect violence in videos is a critical task for various applications, including surveillance, content moderation, and public safety. In this paper, we present a comprehensive dataset for automatic violence detection in videos. The dataset consists of 350 video clips, categorized as either "non-violent" or "violent," and is designed for training and testing violence detection algorithms. To address the challenge of false positives caused by fast movements and similarities between non-violent and violent behaviors, the non-violent clips are specifically recorded to include actions such as hugs, claps, exulting, and gesticulating. The dataset is organized into two main directories, "non-violent" and "violent," with subdirectories "cam1" and "cam2" containing clips recorded from different perspectives using two different cameras.

Keywords — convolutional neural networks (CNNs), deep learning, video analysis, violence detection, automatic, image classification, java script, feature extraction, spatial hierarchies, local receptive fields, filters, pooling, convolutional layers, non-linear activation, parameter sharing.