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Unsupervised Tracking, Roughness and Quantitative Indices

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike many existing techniques. The first target model is constructed using the median filtering based foreground detection technique and after that the target is reconstructed in every frame according to the rough set based feature reduction concept incorporating a measure of indiscernibility instead of indiscernibility matrix. The area of interest is initially defined roughly in every frame based on the object shift in the previous frames, and after reduction of redundant features the object is tracked. The measure of indiscernibility of a feature is defined based on its degree of belonging (DoB) to the target. Three quantitative indices based on rough sets, feature similarity and Bhattacharya distance are proposed to evaluate the performance of tracking and detect the mis-tracked frames in the process of tracking to make those corrected. Unlike many existing measures, the proposed ones do not require to know the ground truth or trajectory of the video sequence. Extensive experimental results are given to demonstrate the effectiveness of the method. Comparative performance is demonstrated both visually and quantitatively.
Wydawca
Rocznik
Strony
63--90
Opis fizyczny
Bibliogr. 30 poz., fot., rys., wykr.
Twórcy
autor
  • Center for Soft Computing Research, Indian Statistical Institute, Kolkata 700 108, India
  • Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India
Bibliografia
  • [1] AVSS-2007: Fourth IEEE Int. Conf. Adv. Video & Signal Based Surveillance, 2007.
  • [2] Black, J., Ellis, T., Rosin, P.: A novel method for video tracking performance evaluation, Proc. Joint IEEE Int. Workshop on VS-PETS, 2003, 125-132.
  • [3] Cheung, S. S., Kamath, C.: A Robust techniques for background subtraction in urban traffic video, Proc. Video Communications and Image Processing, ISPIE Electronic Imaging, 5308, 2004, 881-892.
  • [4] Comaniciu, D., Ramesh, V., Meer, P.: Mean shift: A robust approach towards feature space analysis., IEEE Trans. Patt. Anal. and Machine Intell., 24, 2002, 603619.
  • [5] Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking, IEEE Trans. Pattern Analysis and Machine Intell., 25, 2003, 564-577.
  • [6] Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadows in video streams, IEEE Trans. Patt. Anal. and Machine Intell., 25, 2003, 1337-1342.
  • [7] Dai, S., Ren, W., Gu, F., Huang, H., Chang, S.: Implementation of Robot Visual Tracking System Based on Rough Set Theory, FSKD (2), 2008, 155-160.
  • [8] Fang, H., Jiang, J., Feng, Y.: A fuzzy logic approach for detection of video shot boundaries, Pattern Recognition, 39, 2006, 2092 - 2100.
  • [9] Hassanien, A. E., Abraham, A., Peters, J. F., Schaefer, G.: Overview of rough-hybrid approaches in image processing, Proc. IEEE Conf. on Fuzzy Systems, IEEE Press, N. J., 2008, 2135-2142.
  • [10] Jalal, A. S., Tiwary, U. S.: A Robust Object Tracking Method for Noisy Video using Rough Entropy in Wavelet Domain, IHCI, 2009, 113-122.
  • [11] Komorouski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: a tutorial, Rough Fuzzy Hybridization: A New Trend In Decision-Making (S. K. Pal, A. Skowron, Eds.), Springer, Singapore, 1999, 3-98.
  • [12] Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications, IEEE Trans. Image Process., 17, 2008, 1168-1177.
  • [13] Maggio, E., Cavallaro, A.: Video Tracking - Theory And Practice, Wiley, N. Y., 2010.
  • [14] Needham, C. J., Boyle, R. D.: Performance evaluation metrics and statistics for positional tracker evaluation, Proc. of the Computer Vision Systems: Third International Conference, ICVS, 2003, 278-289.
  • [15] Pal, S. K., Petrosino, A., Maddalena, L., Eds.: Handbook on Soft Computing for Video surveillance, CRC Press, Boca Raton, 2012.
  • [16] Pal, S. K., Shankar, B. U., Mitra, P.: Granular computing, rough entropy and object extraction, Pattern Recogn. Lett., 26, 2005, 2509-2517.
  • [17] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Nor- well, MA, 1992.
  • [18] Peters, J. F., Borkowski, M.: K-means indiscernibility relation over pixels, S. tsumoto, R. Slowinski, J. Komorowski, J.W. Grzymala-Buess, Lecture Notes in artificial Intelligence, Springer-Verlag, Berlin, 2004, 580-535.
  • [19] PETS-2000: IEEE Int. WS Perfor. Evaluation of Tracking and Surveillance, 2000.
  • [20] PETS-2001: IEEE Int. WS Perfor. Evaluation of Tracking and Surveillance, 2001.
  • [21] PETS-2004: IEEE Int. WS Perfor. Evaluation of Tracking and Surveillance and EC Funded CAVIAR project/IST 2001, 2004.
  • [22] Sen, D., Pal, S. K.: Generalized rough sets, entropy, and image ambiguity measures, IEEE Trans. on Systems, Man, and Cyberns., Part B, 39, 2009, 117-128.
  • [23] Shen, C., Kim, J., Wang, H.: Generalized kernel-based visual tracking, IEEE Trans. Circuits and Systems for Video Technology, 20, 2010, 119-130.
  • [24] Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems, Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory (R. Slowinski, Ed.), Kluwer Academic, Dordrecht, 1992.
  • [25] Stauffer, C., Grimson, W. E. L.: Adaptive background mixture models for real-time tracking, Proc Computer Vision and Pattern Recognition, IEEE Computer Society, 1999, 246-252.
  • [26] Swirniaski, R. W.: Rough sets methods in feature reduction and classification, Int. J. of Applied Mathematics and Computer Sc., 11, 2001, 565-582.
  • [27] Tekalp, A. M.: Digital Video Processing, Prentice Hall, N. J., 1995.
  • [28] Yao, Y.: Two semantic issues in a probabilistic rough set model, Fundam. Inform., 108, 2011, 249-265.
  • [29] Yilmaz, A., Javed, O., Shah, M.: Object tracking : a survey, ACM Computing Surveys, 38, 2006, 1264-1291.
  • [30] Zadeh, L.: Fuzzy sets as a basis for theory of possibility, Fuzzy Sets and Systems, 1, 1978, 3-28.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ab784b68-4158-4708-b01a-3c2dd80bd8fd
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