Real-Time Violence Detection Using CNN-LSTM
This addresses the problem of micro-governing violence detection in public surveillance for law enforcement, though it appears incremental as it builds on existing CNN-LSTM approaches.
The paper tackles real-time violence detection from CCTV video feeds by developing a CNN-LSTM model that identifies violent activities, with a hypothesized architecture using probability-driven computation to reduce computational overhead compared to naive processing.
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.