Abstract
Numerous techniques have been evolved for the detection of violence in human beings. Prior detection of human action can help to prevent and control suspicious and criminal activities. The offline video processing system has been used for post-action analysis. We address the violence detection trouble of humans in real-time visual surveillance such as punching, fighting. The present research work proposes a novel framework that processes real-time video data received from fixed cameras installed area of interest under surveillance. To determine the security level, we developed a new algorithm based on the decision-making classifier to recognize the violent situation in real time. In the view of human violence detection, the proposed work is simple and unique. The transition effects observed during violence detection are deliberated in detail. It has wide applications in the area of visual indexing, biometrics, telehealth, and human–computer interaction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Elhamod M, Levine MD (2013) Automated real-time detection of potentially suspicious behavior in public transport areas. IEEE Trans Intell Transport Syst 14(2):688–699. https://doi.org/10.1109/tits.2012.2228640
Chen L, Hsu H, Wang L, Su C (2011) Violence detection in movies. In: 2011 Eighth international conference computer graphics, imaging and visualization, Singapore, pp 119–124. https://doi.org/10.1109/cgiv.2011.14
Donahue JJ et al (2017) Long-Term recurrent convolutional networks for visual recognition and description. IEEE Trans Pattern Anal Mach Intell 39(4):677–691. https://doi.org/10.1109/tpami.2016.2599174
Ji X, Wu O, Wang C, Yang J (2014) Visual feature-based violent video detection. In: 2014 IEEE 3rd International conference on cloud computing and intelligence systems, Shenzhen, pp 619–623. https://doi.org/10.1109/ccis.2014.7175809
Clarin C, Dionisio J, Echavez M, Naval P (2005) DOVE: detection of movie violence using motion intensity analysis on skin and blood. Technical report, University of the Philippines
Chen L, Su C, Hsu H, Violent scene detection in movies. Int J Pattern Recogn Artif Intell 25:1161–1172
D Gupta, A Ahlawat (2017) Usability prediction of live auction using multistage fuzzy system. Int J Artif Intell Appl Smart Devices 5(1)
D Gupta, A Ahlawat (2016) Usability determination using multistage fuzzy system. Procedia Comput Sci. Elsevier, Scopus. https://doi.org/10.1016/j.procs.2016.02.042
Patnaik A, Gupta D (2010) Unique Identification System. Int J Comput Appl
Gupta D, Sagar K (2010) Remote file synchronization single-round algorithms. Int J Comput Appl
Cheng W, Chu W, Ling J (2003) Semantic context detection based on hierarchical audio models. In: Proceedings of the ACM SIGMM workshop on multimedia information retrieval, pp 109–115
Giannakopoulos T, Pikrakis A, Theodoridis S (2006) Violence content classification using audio features. In: Proceedings of the 4th Hellenic conference on artificial intelligence, Crete, Greece, pp 502–507
Zaheer MZ, Kim JY, Kim H-G, Na SY (2015) A preliminary study on deep-leaning based screaming sound detection. In: Proceedings of 5th international conference on IT convergence and security (ICITCS). IEEE, pp 1–4. https://doi.org/10.1109/icitcs.2015.7292925
Giannakopoulos T, Makris A, Kosmopoulos D, Perantonis S, Theodoridis S (2010) Audio-Visual fusion for detecting violent scenes in videos. In: Proceeding of 6th hellenic conference on AI, Lecture Notes in Computer Science, vol 6040. Springer, Berlin, Heidelberg, pp 91–100
Gong Y, Wang W, Jiang S, Huang Q, Wen Gao (2008) Detecting violent scenes in movies by auditory and visual cues. In: Proceedings of the 9th pacific rim conference on multimedia. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg, pp 317–326
Zin TT, Kurohane J (2015) Visual analysis framework for two-person interaction. In: 2015 IEEE 4th global conference on consumer electronics (GCCE), Osaka, pp 519–520. https://doi.org/10.1109/gcce.2015.7398694
Moreira D et al (2017) Temporal robust features for violence detection. In: 2017 IEEE winter conference on applications of computer vision (WACV), Santa Rosa, CA, pp 391–399. https://doi.org/10.1109/wacv.2017.50
Jaiswal SG, Mohod SW (2020) Recapitulating the violence detection systems. In: Kumar A, Mozar S (eds) ICCCE 2019. Lecture Notes in Electrical Engineering, vol 570. Springer, Singapore
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jaiswal, S.G., Mohod, S.W. (2021). Implementation of Violence Detection System using Soft Computing Approach. In: Khanna, A., Gupta, D., Pólkowski, Z., Bhattacharyya, S., Castillo, O. (eds) Data Analytics and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 54. Springer, Singapore. https://doi.org/10.1007/978-981-15-8335-3_56
Download citation
DOI: https://doi.org/10.1007/978-981-15-8335-3_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8334-6
Online ISBN: 978-981-15-8335-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)