Automatic Abnormal Behaviour Detection from videos

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2015 by IJETT Journal
Volume-30 Number-6
Year of Publication : 2015
Authors : Tummala. Yenosh, CH. Kavitha, M. Babu Rao
DOI :  10.14445/22315381/IJETT-V30P255

Citation 

Tummala. Yenosh, CH. Kavitha, M. Babu Rao"Automatic Abnormal Behaviour Detection from videos", International Journal of Engineering Trends and Technology (IJETT), V30(6),294-299 December 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
This paper address the detection of anomalies in real time video surveillance applications such as ATM attacks, accidents, crime, etc. A new technique is proposed to detect anomalies using Joint Kernel Sparsity Model (JKSM) with Multi-Channel Kernel Fuzzy Correlogram (MKFC). Moving objects are segmented by using MKFC in the situations like occlusion, newly appearing or disappearing objects and JKSM is used to detect the anomalies involving in multiple objects. It extracts the features of normal/anomalous events, assign the label to features, and model the prelabeled features as linear combination in a training dictionary. The proposed technique is implemented on two different data sets and the result shows improved precision and recall over the existing technique.

 References

[1] Deval jansari, Shankar parmar ?Novel Object Detection Method Based on the Optical Flow. In proc. ICETCIP conf. Jan. 2015
[2] Nishu Singla ?Motion Detection Based on Frame Differencing Method in international journal of Information & Computation Technology. vol.4 No.15, pp.1559-1565, 2014.
[3]Ssu-Wei Chen, Luke K. Wang, Jen-Hong Lan ?Moving Object tracking Based on Background Subtraction Combined Temporal Difference In proc. ICETCIP conf. Dec. 2011.
[4]Reza Oji An Automatic Algorithm for Object Recognition and Detection Based On ASIFT Key Points Signal & Image Processing: An International Journal (SIPIJ) Vol.3, No.5, October 2012
[5] Mr. Mahesh C. Pawaskar, Mr. N.S.Narkhedeand Mr. Saurabh S. Athalye ?Detection of Moving Object Based on Background Subtraction ?in IJETTCS vol.3, issue.3, may-June 2014.
[6] Faisal I. Bashir, Ashfaq A. Khokhar and Dan Schonfeld ?Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models IEEE Trans. Image Process., vol. 16,No. 7, July 2007.
[7]X. Wang, X. Ma, and W. Grimson, ?Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models, IEEE Trans. Pattern Anal. Mach. Intel., vol. 31, no. 3, pp. 539–555, Mar. 2009.
[8] Andreas Kind, Marc Ph. Stoecklin, and Xenofontas Dimitropoulos Histogram-Based Traffic Anomaly Detection IEEE Trans. NETWORK SERVICE MANAGEMENT.VOL. 6, NO. 2, JUNE 2009
[9] C.-H. Chuang, J.-W. Hsieh, L.-W. Tsai, S.-Y. Chen and K.-C. Fan, ?Carried object detection using ratio histogram and its application to suspicious event analysis, IEEE Trans. Circuits Syst. Video Technol.,vol. 19, no. 6, pp. 911–916, Jun. 2009.
[10] V. Saligrama, J. Konrad, and P. Jodoin, ?Video anomaly identification,IEEE Signal Process. Mag., vol. 27, no. 5, pp. 18–33, Sep.2010.
[11] C. Li, Z. Han, Q. Ye, and J. Jiao, ?Abnormal behaviour detection via sparse reconstruction analysis of trajectory, in Proc. IEEE Int. Conf.Image Graph. Aug. 2011, pp. 807–810.
[12] B. Zhao, L. Fei-Fei, and E. Xing, ?Online detection of unusual events in videos via dynamic sparse coding, in Proc. IEEE Conf. Comput. Vision Pattern Recognit. Jun. 2011, pp. 3313–3320.
[13] C. Piciarelli, C. Micheloni, and G. Foresti, ?Trajectory-based anomalous event detection, IEEE Trans. Circuits Syst. Video Technol., vol. 18, no . 11, pp. 1544–1554, Nov. 2008.
[14]Chris Stauffer, W. Eric L. Grimson Learning Patterns of Activity Using Real-Time Tracking IEEE Trans. Pattern Anal. Mach. Intell., VOL. 22, NO. 8, AUGUST 2000
[15]P. Chiranjeevi and S. Sengupta, ?Detection of moving objects using fuzzy correlogram based background subtraction, in Proc. IEEE ICSIPA, 2011, pp. 255–259.
[16] Pojala Chiranjeevi, Somnath Sengupta Detection of Moving Objects Using Multi-channel Kernel Fuzzy Correlogram Based Background Subtraction IEEE Trans. CYBERNETICS, VOL. 44, NO. 6, JUNE 2014.
[17]. Xuan Mo, Vishal Monga, Raja Balaand Zhigang Fan Adaptive Sparse Representations for VideoAnomaly Detection IEEE Trans.Circuits and Systems for Video Technology, vol. 24, NO. 4, April 2014

Keywords
Video Anomaly, Joint Kernel Sparsity Model, Multi-Channel Kernel Fuzzy Correlogram.