Vision-based Fight Detection from Surveillance Cameras

• URL: http://arxiv.org/abs/2002.04355v1
• Date: Tue, 11 Feb 2020 12:56:29 GMT
• Title: Vision-based Fight Detection from Surveillance Cameras
• Authors: \c{S}eymanur Akt{\i}, G\"ozde Ay\c{s}e Tataro\u{g}lu, Haz{\i}m Kemal Ekenel
• Abstract summary: This paper explores LSTM-based approaches to solve fight scene classification problem. A new dataset is collected, which consists of fight scenes from surveillance camera videos available at YouTube. It is observed that the proposed approach, which integrates Xception model, Bi-LSTM, and attention, improves the state-of-the-art accuracy for fight scene classification.
• Score: 6.982738885923204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-based action recognition is one of the most challenging research topics of computer vision and pattern recognition. A specific application of it, namely, detecting fights from surveillance cameras in public areas, prisons, etc., is desired to quickly get under control these violent incidents. This paper addresses this research problem and explores LSTM-based approaches to solve it. Moreover, the attention layer is also utilized. Besides, a new dataset is collected, which consists of fight scenes from surveillance camera videos available at YouTube. This dataset is made publicly available. From the extensive experiments conducted on Hockey Fight, Peliculas, and the newly collected fight datasets, it is observed that the proposed approach, which integrates Xception model, Bi-LSTM, and attention, improves the state-of-the-art accuracy for fight scene classification.

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