Video Violence Recognition and Localization using a Semi-Supervised Hard-Attention Model

• URL: http://arxiv.org/abs/2202.02212v1
• Date: Fri, 4 Feb 2022 16:15:26 GMT
• Title: Video Violence Recognition and Localization using a Semi-Supervised Hard-Attention Model
• Authors: Hamid Mohammadi, Ehsan Nazerfard
• Abstract summary: Violence monitoring and surveillance systems could keep communities safe and save lives. The current state-of-the-art deep learning approaches to video violence recognition to higher levels of accuracy and performance could enable surveillance systems to be more reliable and scalable. The main contribution of the proposed deep reinforcement learning method is to achieve state-of-the-art accuracy on RWF, Hockey, and Movies datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Empowering automated violence monitoring and surveillance systems amid the growing social violence and extremist activities worldwide could keep communities safe and save lives. The questionable reliability of human monitoring personnel and the increasing number of surveillance cameras makes automated artificial intelligence-based solutions compelling. Improving the current state-of-the-art deep learning approaches to video violence recognition to higher levels of accuracy and performance could enable surveillance systems to be more reliable and scalable. The main contribution of the proposed deep reinforcement learning method is to achieve state-of-the-art accuracy on RWF, Hockey, and Movies datasets while removing some of the computationally expensive processes and input features used in the previous solutions. The implementation of hard attention using a semi-supervised learning method made the proposed method capable of rough violence localization and added increased agent interpretability to the violence detection system.

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