Two-stream Multi-dimensional Convolutional Network for Real-time
Violence Detection
- URL: http://arxiv.org/abs/2211.04255v1
- Date: Tue, 8 Nov 2022 14:04:47 GMT
- Title: Two-stream Multi-dimensional Convolutional Network for Real-time
Violence Detection
- Authors: Dipon Kumar Ghosh and Amitabha Chakrabarty
- Abstract summary: This work presents a novel architecture for violence detection called Two-stream Multi-dimensional Convolutional Network (2s-MDCN)
Our proposed method extracts temporal and spatial information independently by 1D, 2D, and 3D convolutions.
Our models obtained state-of-the-art accuracy of 89.7% on the largest violence detection benchmark dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing number of surveillance cameras and security concerns have made
automatic violent activity detection from surveillance footage an active area
for research. Modern deep learning methods have achieved good accuracy in
violence detection and proved to be successful because of their applicability
in intelligent surveillance systems. However, the models are computationally
expensive and large in size because of their inefficient methods for feature
extraction. This work presents a novel architecture for violence detection
called Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which uses
RGB frames and optical flow to detect violence. Our proposed method extracts
temporal and spatial information independently by 1D, 2D, and 3D convolutions.
Despite combining multi-dimensional convolutional networks, our models are
lightweight and efficient due to reduced channel capacity, yet they learn to
extract meaningful spatial and temporal information. Additionally, combining
RGB frames and optical flow yields 2.2% more accuracy than a single RGB stream.
Regardless of having less complexity, our models obtained state-of-the-art
accuracy of 89.7% on the largest violence detection benchmark dataset.
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