Real-Time Violence Detection Using CNN-LSTM
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 15 Jul 2021.
Builds on
5 of 15 references.
References · 5
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
- 2017Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensorscited by 24
- 2017Learning to Detect Violent Videos using Convolutional Long Short-Term Memorycited by 23
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Citing papers per year: 2024: 1.
1 paper.