Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling

Authors

  • Manisha Mudgal Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India
  • Deepika Punj Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India https://orcid.org/0000-0001-8191-096X
  • Anuradha Pillai Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2017

Keywords:

Action recognition, surveillance systems, gaussian mixture model, violence action

Abstract

Research in the field of image processing and computer vision for recognition of suspicious activity is growing actively.   Surveillance systems play a key role in monitoring of sensitive places such as airports, railway stations, shopping complexes,   roads, parking areas, roads, banks. For a human it is very difficult to monitor surveillance videos continually, therefore a smart and intelligent system is required that can do real time monitoring of all activities and can categories between usual and some abnormal activities. In this paper many different abnormal activities has been discussed.  More focuses is given to violence activity like hitting, slapping, punching etc. For this large human action dataset like UCF101, Kaggel is required. This paper proposes a method to model violence actions using Gaussian Mixture Model with Universal Attribute Model. In this action vector is used to remove redundant attributes and get a low dimensional relevant action vectors.

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Author Biographies

Manisha Mudgal, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Manisha Mudgal is a PHD scholar in Department of Computer Engineering at JC BOSE University of Science and Technology YMCA, Faridabad, India. She has done her M. Tech from M D University Haryana, India. She has successfully published 5 papers in Reputed National and International Journals. Her subjects of interest include Data Mining, Information Retrieval, and Machine Learning.

Deepika Punj, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Deepika Punj is working as Assistant Professor in Department of Computer Engineering at JC BOSE University of Science and Technology YMCA, Faridabad, India. She has done Ph.D in Computer Engineering. She is having 14 years of experience in teaching. She has published more than 25 papers in Reputed National and International Journals. Her research interests include Data Mining, Deep Learning, Machine Learning and Internet Technologies.

Anuradha Pillai, Department of Computer Engineering, JC BOSE UST YMCA Faridabad, Haryana, India

Anuradha Pillai is an Associate Professor in the Department of Computer Engineering, JC Bose University of Science and Technology, YMCA, Faridabad, Haryana, India. She received Ph.D. in Computer Engineering from Maharishi Dayanand University, Rohtak. She published more than 60 papers in reputed international journals and successfully guided 4 PhD students. Her subjects of interest include Data Mining, Information Retrieval, Hidden web, Web Mining and Social Networks.

References

Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011). Robust video surveillance for fall detection based human shape deformation. IEEE Trans Circuit Syst Video Technol 21(5):611–622.

Seebamrungsat J, Praising S, Riyamongkol P (2014). Fire detection in the buildings using image processing.Third ICT international student project conference (ICT-ISPC), 2014, IEEE, pp. 95–98.

Z. Wang, Y. Wang, L. Wang, Y. Qiao, Codebook enhancement of vlad representation for visual recognition, in: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 1258–1262.

Y. Zhang, H. Lu, L. Zhang, X. Ruan, Combining motion and appearance cues for anomaly detection, Pattern Recognition 51 (2016) 443–452.

Nam Y (2016). Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes. Multimed Tools Appl 75(12):7003–7028.

J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

J. F. Henriques, R. Caseiro, P. Martins, J. Batista, High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 583–596.

D. G. Lee, H. I. Suk, S. K. Park, S. W. Lee, Motion influence map for unusual human activity detection and localization in crowded scenes, IEEE Transactions on Circuits and Systems for Video Technology 25(10) (2015) 1612–1623.

Zin TT, Tin P, Toriu T, Hama H (2012b). A probability-based model for detecting abandoned objects in video surveillance systems. In: Proceedings of the world congress on engineering, vol 2.

Tripathi V, Gangodkar D, Latta V, Mittal A (2015). Robust abnormal event recognition via motion and shape analysis at ATM installations. J Electr Comput Eng 2015.

Mohammad Nakib, Rozin Tanvir Khan, Md. Sakibul Hasan, Jia Uddin, “Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network’, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), IEEE 2018.

Alkesh Bharati, Dr Sarvanaguru RA, “Crime Prediction and Analysis Using Machine Learning”, International Research Journal of Engineering and Technology IRJET), (2018).

Babakura A, Sulaiman MN, Yusuf MA. Improved method of classification algorithms for crime prediction. InBiometrics and Security Technologies (ISBAST), 2014 Aug 26 (pp. 250–255). IEEE.

K. Soomro, A. R. Zamir, M. Shah, UCF101: A dataset of 101 human actions classes from videos in the wild, CoRR abs/1212.0402. URL http://arxiv.org/abs/1212.0402

Mohammad Nakib, Rozin Tanvir Khan, Md. Sakibul Hasan, Jia Uddin, “Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network”, International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), IEEE 2018.

Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30(3):555–560.

D. Xu, Y. Yan, E. Ricci, N. Sebe, Detecting anomalous events in videos by learning deep representations of appearance and motion, Computer Vision and Image Understanding 156 (Supplement C) (2017) 117–127, image and Video Understanding in Big Data.

Dimitropoulos K, Barmpoutis P, Grammalidis N (2015). Spatio-temporal flame modelling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans Circuit Syst Video Technol 25(2):339–351.

Ghazal M, Vázquez C, Amer A (2012). Real-time vandalism detection by monitoring object activities. Multimed Tools Appl 58(3):585–611.

Wiliem A, Madasu V, Boles W, Yarlagadda P (2012). A suspicious behaviour detection using a context space model for smart surveillance systems. Comput Vis Image Underst 116(2):194–209.

P. Kenny, G. Boulianne, P. Dumouchel, Eigenvoice modeling with sparse training data, IEEE Transactions on Speech and Audio Processing 13(3) (2005) 345–354.

Debaditya Roy, K. Sri Rama Murty, and C. Krishna Mohan (2018). Unsupervised Universal Attribute Modeling for Action Recognition, IEEE Transactions on Multimedia.

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Published

2021-02-18

Issue

Section

Data Science and Artificial Intelligence: Architecture, Use Cases, and Challenge