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Video based Action Recognition for Behavior understanding - A Study
Abstract
Development of automatic intelligent video content analysis system is an important goal of both the commercial and public sector surveillance industries. The main tasks in automatic visual surveillance system include moving object detection, static object detection, object classification, object tracking, action recognition and behavior understanding. Oobjects can be moving or static and can behave normally or abnormally. The abnormality in a scene is also termed as suspicious object, suspicious event, irregular behavior, unusual event, anomaly etc. Detection of suspicious behavior of object involves modeling and classification of actions. Behavior understanding task becomes more and more difficult as complexity of activity increases. Different methodologies studied in this paper identify various problems in video based action recognition system and give direction for further research in this area.
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References
Su Kim, Hong Choi et.al. “Intelligent Video Surveillance – A survey “, in International Journal of Control, Automation and Systems 8(5); PP 926-939, 2010
M. Sivarathinabala, S. Abirami, “An intelligent video surveillance Framework for remote Monitoring”, International Journal of Engineering Science and Innovative Technology, Volume 2, Issue 2, March 2013, ISSN: 2319-5967
Lovely June Sanglyne, Gypsy Nandi, “Object Detection and Extraction of Moving Human Silhouethes in Video Surveillance Systems”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 10, October 2014, ISSN 2277 128X
Sarvesh Vishwakarma, Anupam Agrawal, “A Survey on Activity Recognition and Behavior Understanding in Video Surveillance”, Springer – Verlag Berlin Heidelberg 2012
Paulo Vinicius Koerich Borges et al., “Video Based Human Behavior Understanding: A Survey”, IEEE 2013
Chen, Y., Zhang, L., Lin, B., Xu, Y., Ren, X.: Fighting detection based on optical flow context histogram. In: Proc. of IEEE 2nd Int. Conf. on Innovations in Bio-inspired Computing and Applications, pp. 95–98 (2011)
Rub´en Heras Evangelio and Thomas Sikora, “Complementary Background Models for the Detection of Static and Moving Objects in Crowded Environments”, 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2011
Samuel Schultery , Christian Leistnerz et.al, “Accurate Object Detection with Joint Classification-Regression Random Forests”, In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
Dawei Li, Lihong Xu, Erik D. Goodman, “Illumination-Robust Foreground Detection in a Video Surveillance System”, IEEE Trans.on Circuits and systems For Video Technology, VOL. 23, No. 10, October 2013
Kalyan Kumar Hati, Pankaj Kumar Sa, and Banshidhar Majhi, “Intensity Range Based Background Subtraction For Effective Object Detection”, IEEE Signal Processing Letters, Vol. 20, No. 8, August 2013
Tianzhu Zhang, Si Liu, Changsheng Xu ,Hanqing Lu, “Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes”, IEEE Transactions On Industrial Informatics, Vol. 9, No. 1, February 2013
A. Blum and T. Mitchell, “Combining labeled and unlabeled data with cotraining,” in Proc. 11th Annu. Conf. Computational Learning Theory, 1998.
R. Cutler and L. Davis, “Robust real-time periodic motion detection, analysis, and applications,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 22, no. 8, pp. 781–796, Aug. 2000
A. Levin, P. Viola, and Y. Freund, “Unsupervised improvement of visual detectors using co-training,” in Proc. ICCV, 2003.
L. Zhang, S. Li, X. Yuan, and S. Xiang, “Real-time object classification in video surveillance based on appearance learning,” in Proc. IEEE Int. Workshop Visual Surveillance in Conjunction With CVPR, 2007
M. Kafai and B. Bhanu, “Dynamic Bayesian networks for vehicle classification in video,” IEEE Trans. Ind. Inf., vol. 8, no. 1, pp. 100–109, Feb. 2012
Chiverton, J., Mirmehdi, M., Xie, X. “On-line learning of shape Information for object segmentation and tracking.” In: Proc. Of British Machine Vision Conference, pp. 1–11 (2009)
Chen, Q., Sun, Q.S., Heng, P.A., Xia, D.S.: Two-stage object tracking method based on kernel and active contour. IEEE Trans.Circuits Syst. Video Technol. 20(4), 605–609 (2010)
Chai, Y., Shin, S., Chang, K., Kim, T.: Real-time user interface using particle filter with integral histogram. IEEE Trans. Consum. Electron. 56(2), 510–515 (2010)
Cai, L., He, L., Yamashita, T., Xu, Y., Zhao, Y., Yang, X. “Robust contour tracking by combining region and boundary information.”IEEE Trans. Circuits Syst. Video Technol. 21(12), 1784–1794(2011)
Chiverton, J., Xie, X., Mirmehdi, M. “Automatic bootstrapping and tracking of object contours.” IEEE Trans. Image Process. 21(3), 1231–1245 (2012)
Vishwakarma, S., Agrawal, A.: “A novel approach for feature quantization using one-dimensional histogram.” In: Annual IEEE India Conference (INDICON), pp. 1–4 (2011)
Vishwakarma, S., Sapre, A., Agrawal, A.: “Action recognition using cuboids of interest points.” In IEEE Int. Conf. on Signal Processing, Communications and Computing (ICSPCC), pp. 1– 6 (2011)
MahfuzulHaque, and ManzurMurshed, "Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed," In Proc of the 2012 IEEE International Conference on Multimedia and Expo Workshops (ICMEW '12), Melbourne, Australia, pp.396-401, July 2012
Ronald Poppe, “A Survey on Vision-Based Human Action Recognition,” In Image and Vision Computing Journal, Volume 28, Issue 6, pp. 976–990, June 2010.
Saad Ali, and Mubarak Shah, "Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning," In the IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 32 , Issue 2, pp. 288 - 303, February 2010.
Xu, M., Zuo, L., Iyengar, S., Goldfain, A., DelloStritto, J, “ A semi-supervised hidden Markov model-based activity monitoring system.”, In 33rd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Boston, Massachusetts USA, pp. 1794–1797 (2011)
Tian Wang, Hichem Snoussi, “Detection of Abnormal Visual Events via Global Optical Flow Orientation Histogram”, IEEE Trans. On Information Forensics And Security, Vol 9, No 6, June 2014
Carolina Garate, Sofia Zaidenberg, Julien Badie, Francois Bremond, “ Group Tracking and Behavior recognition in Long Video Surveillance Sequences”, VISAPP- 9th International Joint onference on Computer vision, Imaging and Computer Graphics, Jan 2014
Sarvesh Vishwakarma, Anupam Agrawal, “A survey on activity recognition and behavior understanding in video surveillance”, Springer- Verlag BerlinHeidelberg 2012
Ahmed Taha, Hala H. Zayed et.al, “On Behavior Analysis in Video Surveillance”, In proc of The 6th International Conference on Information Technology 2013
Sofia Zaidenberg, Bernard Boulay et.al, “ A Generic Framework for Video Understanding Applied to Group Behavior Recognition”, In International conference on Advanced video Signal based surveillance (AVSS) 2012
Jasper R. Van Huis, Henri Bouma et.al., “Track-based event recognition in a realistic crowded environment”, In proc of Society of Photo-Optical Instrumentation Engineers, SPIE, 2014
Maria Anderson, Fredrik Gustafsson et. Al, “Recognition of Anomalous Motion patterns in Urban Surveillance”, Selected Topics in Signal Processing, IEEE Journal Volume:7 , Issue: 1 22 January 2013
Ramanathan, M.; et.al, "Human Action Recognition With Video Data: Research and Evaluation Challenges," in Human-Machine Systems, IEEE Transactions on vol.44, no.5, pp.650-663, Oct. 2014
Yan Song et.al, “Describing Trajectory of Surface Patch for human Action Recognition on RGB and Depth Videos”, IEEE Signal Processing Letter, Vol. 22, No. 4, April 2015
Nick C Tang et.al, “Robust Action Recognition via Borrowing Information Across Video Modalities”, IEEE Transactions on Image Processing, Vol. 24, No. 2, February 2015
Daniel Weinland et. al, “A survey of vision based methods for action representation, segmentation and recognition”, Computer vision and Image Understanding, 2011 – Elsevier
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