Detection of fence climbing using activity recognition by Support Vector Machine classifier

Detection of person climbing the fence is a vital event, taking borders and restricted zones securities into the consideration. In this paper, a model is presented which detects the human crossing a fence. Entire event of climbing a fence involve activities like walking, climbing up and climbing down. Moving person is detected by background subtraction algorithm. Centroid of the blob and centroid variations along the frames are considered as features. Support Vector Machine classifier is used for detection of walking, climbing up and climbing down activities. The experiments show the best results in detection of human crossing a fence compared to the existing state of art methods.

[1]  Li Guo-hui,et al.  A surveillance activity recognition model based on Hidden Markov Model , 2012 .

[2]  Jake K. Aggarwal,et al.  Detection of Fence Climbing from Monocular Video , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[4]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..

[5]  Anupam Agrawal,et al.  A survey on activity recognition and behavior understanding in video surveillance , 2012, The Visual Computer.

[6]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[8]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Theodore Berger,et al.  Intelligent fence intrusion detection system: detection of intentional fence breaching and recognition of fence climbing , 2008, 2008 IEEE Conference on Technologies for Homeland Security.

[10]  M.C. Maki,et al.  Fiber optic fence sensor developments , 2004, IEEE Aerospace and Electronic Systems Magazine.

[11]  Edward J. Delp,et al.  Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization , 2011, Electronic Imaging.

[12]  Zhiquan Wang,et al.  Recognition of human activities using SVM multi-class classifier , 2010, Pattern Recognit. Lett..

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[15]  Abhishek Kumar,et al.  Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals , 2014, 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom).

[16]  Sathishkumar Sivagurunathan,et al.  Automatic detection of entry into a restricted area , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[17]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[18]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[19]  Chin-Lun Lai,et al.  An Intelligent Virtual Fence Security System for the Detection of People Invading , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[20]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[21]  J. de Vries.  A low cost fence impact classification system with neural networks , 2004, 2004 IEEE Africon. 7th Africon Conference in Africa (IEEE Cat. No.04CH37590).