Violence Recognition using Deep CNN for Smart Surveillance Applications
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참고문헌
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초록
Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.
Abstract
Due to the recent developments in computer vision technology, complex actions can be recognized with reasonable accuracy in smart cities. In contrast, violence recognition such as events related to fight and knife, has gained less attention. The capability of visual surveillance can be used for detecting fight in streets or in prison centers. In this paper, we proposed a deep learning-based violence recognition method for surveillance cameras. A convolutional neural network (CNN) model is trained and fine-tuned on available benchmark datasets of fights and knives for violence recognition. When an abnormal event is detected, an alarm can be sent to the nearest police station to take immediate action. Moreover, when the probabilities of fight and knife classes are predicted very low, this situation is considered as normal situation. The experimental results of the proposed method outperformed other state-of-the-art CNN models with high margin by achieving maximum 99.21% accuracy.
참고문헌 (15)
- Nievas, E.B., "Violence detection in video using computer vision techniques", International conference on Computer analysis of images and patterns (2011), Springer
- Bilinski, P., "Human violence recognition and detection in surveillance videos", 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2016), IEEE
- Giannakopoulos, T., "A multi-class audio classification method with respect to violent content in movies using bayesian networks", Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on (2007), IEEE
- Zajdel, W., "CASSANDRA: audio-video sensor fusion for aggression detection", Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on (2007), IEEE
- Nam, J., "Audio-visual content-based violent scene characterization", Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on (1998), IEEE
- Datta, A, "Personon-person violence detection in video data", Pattern Recognition, 2002. Proceedings. 16th International Conference on (2002), IEEE
- Lin, J., "Violence detection in movies with auditory and visual cues", Computational Intelligence and Security (CIS), 2010 International Conference on (2010), IEEE
- Sajjad, M., "Raspberry Pi Assisted Facial Expression Recognition Framework for Smart Security in Law-Enforcement Services", Information Sciences (2018)
- Giannakopoulos, T., "Violence content classification using audio features", Hellenic Conference on Artificial Intelligence (2006), Springer
- Ullah, A., "Action recognition in video sequences using deep Bi-directional LSTM with CNN features", IEEE Access, 6, pp.1155~1166 (2018)
- Sjöberg, M., "The MediaEval 2014 Affect Task: Violent Scenes Detection" (2014), MediaEval
- Grega, M., "Automated detection of firearms and knives in a CCTV image", Sensors, 16(1), pp.47~ (2016)
- Demarty, C.-H., "VSD, a public dataset for the detection of violent scenes in movies:design, annotation, analysis and evaluation", Multimedia Tools and Applications, 74(17), pp.7379~7404 (2015)
- Zhou, P., "Violent Interaction Detection in Video Based on Deep Learning", Journal of Physics: Conference Series (2017), IOP Publishing
- Veenendaal, A., "Fight and Aggression Recognition using Depth and Motion Data", Computer Science and Emerging Research Journal (2016)