Intelligent Image Sensing for Crime Analysis: A ML Approach towards Enhanced Violence Detection and Investigation

TL;DR

This paper presents an ML-based framework using 3D CNNs and LSTMs for automatic violence detection in video streams, aiming to improve surveillance efficiency and accuracy in crime analysis.

Contribution

It introduces a novel integrated system combining 3D CNNs and bidirectional LSTMs for violence detection and classification, with real-time processing on a Raspberry Pi platform.

Findings

Enhanced detection accuracy over traditional methods

Efficient real-time processing on embedded devices

Effective classification of various violent events

Abstract

The increasing global crime rate, coupled with substantial human and property losses, highlights the limitations of traditional surveillance methods in promptly detecting diverse and unexpected acts of violence. Addressing this pressing need for automatic violence detection, we leverage Machine Learning to detect and categorize violent events in video streams. This paper introduces a comprehensive framework for violence detection and classification, employing Supervised Learning for both binary and multi-class violence classification. The detection model relies on 3D Convolutional Neural Networks, while the classification model utilizes the separable convolutional 3D model for feature extraction and bidirectional LSTM for temporal processing. Training is conducted on a diverse customized datasets with frame-level annotations, incorporating videos from surveillance cameras, human recordings, hockey fight, sohas and wvd dataset across various platforms. Additionally, a camera module integrated with raspberry pi is used to capture live video feed, which is sent to the ML model for processing. Thus, demonstrating improved performance in terms of computational resource efficiency and accuracy.