Author : Shilpashree S 1
Date of Publication :7th May 2016
Abstract: Human tracking and Activity recognition is a field of computer vision which has shown great progress in the past decade. Starting from simple single person activities, research in activity recognition is moving towards more complex scenes involving multiple objects and natural environments. The main challenges in the task include being able to localize and recognize events in a video and deal with the large amount of variation in viewpoint, speed of movement and scale. It has gained more interest of late, among video processing community because they find various applications in automatic surveillance, monitoring systems, video indexing and retrieval, robot motion, human – computer interaction and segmentation. The proliferation of surveillance cameras and smart-phones has dramatically changed the video capture landscape. There is more video data generated each day than ever before, it is more diverse and its importance has reached beyond security and entertainment (e.g., healthcare, education, and environment).This project deals with the single person tracking and action recognition of that person. And also it deals with combining the advantages of both tracking and action recognition in a single framework. Here based on STIP single person tracking is done, kNN classifier is used for action classification and recognition. Finally, coding is done in Matlab.
Reference :
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[1] Chaudhry R, Ravichandran A, Hager G, Vidal R (2009) Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: IEEE conferences on computer vision and pattern recognition (CVPR’2009).
[2] Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space-time shapes. IEEE Trans Pattern Anal Mach Intell 29(29):2247–2253.
[3] Sadek S, Al-Hamadi A, Michaelis B, Sayed U (2012) Chord length shape features for human activity recognition. ISRN machine vision, article ID 872131.
[4] Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv 43
[5] Monnet A, Mittal A, Paragios N, Ramesh V (2003) Background modeling and subtraction of dynamic scenes. In: IEEE international conferences on computer vision (ICCV’2003), vol 2, pp 1305–1312
[6] Piccardi M (2004) Background subtraction techniques: a review. In: Proceeding IEEE international conferences on systems, man and cybernetics, vol 4, pp 3099–3104 [7] Deza E, Deza MM (2009) Encyclopedia of distances. Springer, Berlin.
[8] Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, NewYork [9] Elkan C , Nearest neighbor classification,2011 [10] Ben-Hur A, Weston J (2010) A user’s guide to support vector machines. Humana Press a part of Springer Science + Business Media, LLC 2010, New York
[11] Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowledge Disc 2(2):121–167
[12] Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:2
[13] Gunn SR (1998) Support vector machines for classification and regression. University of Southampton, Technical report MP-TR-98-05, Image speech and intelligent systems group
[14] Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45
[15] Harris C, Stephens M (1988) A combined corner and edge detector. In: 4th Alvey vision conferences, pp 147– 151
[16] Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 [17] Mikolajczyk K, Schmid C (2002) An affine invariant interest point detector. In: Proceedings of the 7th European conferences on computer vision (ECCV’2002).
[18] Comaniciu D, Ramesh V, Meer P (2003) Kernelbased object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575 [19] Shi J, Tomasi C (1994) Good features to track. In: IEEE conferences on computer vision and pattern recognition (CVPR‘1994), pp 593–600