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The increasing number of surveillance cameras and security concerns have madeautomatic violent activity detection from surveillance footage an active areafor research. Modern deep learning methods have achieved good accuracy inviolence detection and proved to be successful because of their applicabilityin intelligent surveillance systems. However, the models are computationallyexpensive and large in size because of their inefficient methods for featureextraction. This work presents a novel architecture for violence detectioncalled Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which usesRGB frames and optical flow to detect violence. Our proposed method extractstemporal and spatial information independently by 1D, 2D, and 3D convolutions.Despite combining multi-dimensional convolutional networks, our models arelightweight and efficient due to reduced channel capacity, yet they learn toextract meaningful spatial and temporal information. Additionally, combiningRGB 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-artaccuracy of 89.7% on the largest violence detection benchmark dataset.

Two-stream Multi-dimensional Convolutional Network for Real-time Violence Detection