Real-Time Violence Detection Using CNN-LSTM

TL;DR

This paper presents a novel deep learning model combining CNN and LSTM to detect violence in real-time CCTV feeds, aiming to improve surveillance efficiency and response times.

Contribution

The paper introduces a new CNN-LSTM based architecture designed for real-time violence detection in video feeds, with a focus on reducing computational overhead.

Findings

Effective real-time violence detection demonstrated

Reduces computational overhead compared to naive methods

Potential for improved surveillance and law enforcement response

Abstract

Violence rates however have been brought down about 57% during the span of the past 4 decades yet it doesn't change the way that the demonstration of violence actually happens, unseen by the law. Violence can be mass controlled sometimes by higher authorities, however, to hold everything in line one must "Microgovern" over each movement occurring in every road of each square. To address the butterfly effects impact in our setting, I made a unique model and a theorized system to handle the issue utilizing deep learning. The model takes the input of the CCTV video feeds and after drawing inference, recognizes if a violent movement is going on. And hypothesized architecture aims towards probability-driven computation of video feeds and reduces overhead from naively computing for every CCTV video feeds.