Open Access Open Access  Restricted Access Subscription or Fee Access

Video Surveillance and Object Tracking: A Survey

Akriti Sahu, Rajesh Tiwari

Abstract


The multiple object tracking from video sequences has been an open research question for so many years. It remained a demanding research for the tracking of motion and simultaneous object detection within a domain range of image processing and computer vision. The tracking methodologies involve object segmentation, background subtraction, feature descriptors like mean-shift, particle filter. Thus, the performance of such tracking methods varies with the computational complexity which relies on the background information. This paper puts forth a detailed survey of the state of the art attempts made in the direction with their positive and negative aspects.

Keywords


background modeling, feature descriptor, object tracking, computer vision

Full Text:

PDF

References


Serby D, Meier EK, Gool LV. "Probabilistic Object Tracking Using Multiple Features", IEEE Proc. of International Conf on Pattern Recognition Intelligent Transportation Systems. 2004; 6: 43-53p.

Li L, Ranganath S, Weimin H, Sengupta K. "Framework for Real-Time Behavior Interpretation from Traffic Video." IEEE Tran. On Intelligen Transportation Systems. 2005; 6(1): 43-53p.

Kumar P, Weimin H, Gu IU, Tian Q. "Statistical Modeling of Complex Backgrounds for Foreground Object Detection." IEEE Trans. On Image Processing. Nov. 2004; 13(11): 43-53p.

Zivkovi Z. "Improving the selection of feature points for tracking." In Pattern Analysis and Applications. Copyright Springer-Verlag London Limited, 2004; 7(2).

Lou J, Tan T, Hu W, Yang H, Maybank SJ. "3D Model-Based Vehicle Tracking." IEEE Trans. on Image Processing. Oct. 2005; 14: 1561-9p.

Xu N, Ahuja N. ‘Object contour tracking using graph cuts based active contours.’ Proceedings of International Conference on Image Processing. 2002; 3: 277-80p.

Dokladal P, Enficiaud R, Dejnozkova E. ‘Contour-based object tracking with gradient-based contour attraction field.’ Proceedings of EEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04). 2004: 17-20p.

Chen T. ‘Object Tracking Based on Active Contour Model by Neural Fuzzy Network’, Proceedings of IITA International Conference on Control Automation and Systems Engineering. 2009; 570-4p.

Chen Q, Sun QS, Heng PA, Xia DS. ‘Two-Stage Object Tracking Method Based on Kernel and Active Contour.’ IEEE Transactions on Circuits and Systems for Video Technology. 2010: 605-9p.

Pu B, Zhou F, Bai X. ‘Particle Filter Based on Color Feature with Contour Information Adaptively Integrated for Object Tracking.’ Fourth International Symposium on Computational Intelligence and Design. 2011; 359-62p.

Lu X, Song L, Yu S, Ling N. “Object Contour Tracking Using Multi-feature Fusion based Particle Filter.” Proceedings of IEEE Conference on Industrial Electronics and Applications (ICIEA). 2012: 237–42p.

Hu W, Zhou X, Li W, Luo W, Zhang X, Maybank S. ‘Active Contour -Based Visual Tracking by Integrating Colors, Shapes, and Motions.’ IEEE Transactions on Image Processing. 2013; 22(5): 1778–92p.

Rajabi H, Nahvi M. ‘Modified contour-based algorithm for multiple objects tracking and detection.’ Proceedings of 3th International eConference on Computer and Knowledge Engineering (ICCKE). 2013: 235-9p.

Li N, Liu L, Xu D. Corner feature based object tracking using Adaptive Kalman Filter, Signal Processing, 2008. ICSP 2008. Proceedings of 9th International Conference. 2008: 1432–5p.

Xue C. Image Process. Lab., CAS, Changchun ; Ming Zhu; Ai-hua Chen, A Discriminative Feature-Based Mean-shift Algorithm for Object Tracking, Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium. 2008: 217–20p.

Yang W, Li J; Liu J, Shi D. A Novel Layered Object Tracking Algorithm for Forward-looking Infrared Imagery Based on Mean Shift and Feature Matching, Computer Science and Information Technology, 2009. ICCSIT 2009. Proceedings of 2nd IEEE International Conference. 2009: 188–191p.

Aibin C, Zixing C, Deyi D. An Image Tracking Algorithm Based on Object Center location and Image NMI Feature, Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Proceedings of Sixth International Conference. 2009; 5: 359–63p.

Rahman MS, Saha A, Khanum S. “Multi-Object Tracking in Video Sequences Based on Background Subtraction and SIFT Feature Matching”, Computer Sciences and Convergence Information Technology, 2009. ICCIT'09 Proceedings of Fourth International Conference. 2009: 457–62p.

Fazli S, Pour HM, Bouzari H. Particle Filter based Object Tracking with Sift and Color Feature, Machine Vision, 2009. ICMV '09. Proceedings of the Second International Conference. 2009: 89–93p.

Bai KJ. A new object tracking algorithm based on Mean Shift in 4-D State Space and On-line Feature Selection, Information and Computing (ICIC), 2010 Third International Conference. 2010: 39–42p.

Miao Q, Wang G, Lin X, Wang Y. Scale and rotation invariant feature based object tracking via modified on-line boosting , Image Processing (ICIP), 2010 17th IEEE International Conference. 2010: 3929–32p.

22 Fan L, Riihimaki M, Kunttu I. A feature-based object tracking approach for realtime image processing on mobile devices Image Processing (ICIP), 2010 17th IEEE International Conference. 2010: 3921–4p.

Kim T, Lee S, Paik J. Combined shape and feature-based video analysis and its application to non-rigid object tracking, IET Image Processing. 2011; 5(1): 87–100p.

Fan L. “A Feature-based Object Tracking Method Using Online Template Switching and Feature Adaptation, Image and Graphics (ICIG), 2011 Sixth International Conference. 2011: 707–13p.

Alvarez MS, Regazzoni CS. ‘Extended feature-based object tracking in presence of data association uncertainty.” Proceedings of International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 8th IEEE. 2011: 136–41p.

Biresaw TA. Online failure detection and correction for Bayesian sparse feature-based object tracking, Advanced Video and Signal-Based Surveillance (AVSS), 8th IEEE International Conference. 2011: 320–4p.

Oh CM, Lee CW, Lee GS. ‘Multi-Part SIFT feature based particle filter for rotating object tracking’, Informatics, Electronics & Vision (ICIEV), International Conference. 2012: 1016–20p.

Shen HY, Sun SF, Ma XB, Xu YC, Lei BJ. ‘Comparative study of color feature for particle filter based object tracking.’ Proceedings of International Conference on Machine Learning and Cybernetics (ICMLC). 2012: 1104–10p.

Xiao Q, Liu X, Liu M. ‘Object Tracking Based on Local Feature Matching Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium. 2012; 1: 399–402p.

Mahendran S, Vaithiyanathan D, Seshasayanan R. ‘Object tracking system based on invariant features’ Proceedings of International Conference on Communications and Signal Processing (ICCSP). 2013: 1138–42p.

Ning J, Yu W, Yang S. An active contour tracking method by matching for ground and background simultaneously, 2013.

Liu Y, Lu Y, Shi Q, Ding J. ‘Optical Flow Based Urban Roawd Vehicle Tracking, Computational Intelligence and Security (CIS), 2013 9th International Conference. 2013: 391-5p.

Xu D, Hwang JN, Yu J. ‘An accurate region based object tracking for video sequences’, IEEE 3rd Workshop Multimedia Signal Processing. 1999: 271-6p.

Chuang G, Ming-Chieh L. ‘Semantic video object tracking using region-based classification, International Conference on Image Processing (ICIP'98). 1998; 3: 643p.

Hariharakrishnan K, Schonfeld D. “Fast object tracking using adaptive block matching” IEEE Transactions on Multimedia. 2005; 7 (5): 853–9p.

Andrade EL, Woods JC, Khan E, Ghanbari M. Region-based analysis and retrieval for tracking of semantic objects and provision of augmented information in interactive sport scenes. IEEE Transactions on Multimedia. Dec. 2005; 7 (6): 1084-96p.

Wei FT, Chou ST, Lin CW. ‘A region-based object tracking scheme using Adaboost-based feature selection.’ IEEE International Symposium on Circuits and Systems. 2008; I: 2753–6p.

Kim HB, Sim KB. ‘A particular object tracking in an environment of multiple moving objects.’ Proceedings of International Conference on Control Automation and Systems (ICCAS). 2010: 1053–6p.

Khraief C, Bourouis S, Hamrouni K. Unsupervised video objects detection and tracking using region based level-set. Proceedings of International Conference on Multimedia Computing and Systems (ICMCS). 2012: 201–6p.

Varas D, Marques F. ‘A region-based particle filter for generic object tracking and segmentation’, Proceedings of 19th IEEE International Conference on Image Processing (ICIP). 2012: 1333–6p.

Kumar S, Narayanan MS, Singhal P, Corso JJ, Krovi V. “Product of tracking experts for visual tracking of surgical tools.” Proceedings of IEEE International Conference on Automation Science and Engineering (CASE). 2013: 480–5p.

Wu X, Mao X, Chen L, Compare A. “Combined Motion and Region-Based 3D Tracking in Active Depth Image Sequence.” Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. 2013: 1734–9p.

Yilmaz A, Javed O, Shah M. "Object Tracking: A Survey." ACM Computing Surveys. 2006; 38(4).

Yang WB, Fang B, Tang YY, Shang ZW, Li DH. "Sift features based object tracking with discrete wavelet transform." Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR. 2009: 380-5p.

Yang H, Shao L, Zheng F, Wangd L, Song Z. "Recent advances and trends in visual tracking: A review." Elsevier Neurocomputing. 2011; 74 : 3823–31p.

Yi F, Moon I. "Image Segmentation: A Survey of Graph-cut Methods." Proceedings of International Conference on Systems and Informatics (ICSAI 2012), 2012.

Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans. Patt. Analy. Mach. Intell. 2000; 22(8): 888–905p.

Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Analy. Mach. Intell. 2002; 24(5): 603–19p.

Pham V, Vo P, Thanh HV, Hoai BL. "GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction." IEEE-RIVF 2010 Proceedings of International Conference on Computing and Telecommunication Technologies. Nov. 01-4, 2010.

Stauffer C, Grimson W. Learning patterns of activity using real time tracking. IEEE Trans. Patt. Analy. Mach. Intell. 2000; 22(8): 747–67p.

Rittscher J, Kato J, Joga S, Blake A. A probabilistic background model for tracking. In European Conference on Computer Vision (ECCV). 2000; 2: 336–50p.

Stenger B, Ramesh V, Paragios N, Coetzee F, Burmann J. Topology free hidden markov models: Application to background modeling. In IEEE International Conference on Computer Vision (ICCV). 2001: 294–301p.

Viola P, Jones M, Snow D. Detecting pedestrians using patterns of motion and appearance. In IEEE International Conference on Computer Vision (ICCV). 2003: 734–41p.

Papageorgiou C, Oren M, Poggio T. “A general framework for object detection”. In IEEE International Conference on Computer Vision (ICCV). 1998: 555–62p.

Tanizaki H. “Non-gaussian state-space modeling of nonstationary time series.” J. Amer. Statist. Assoc. 1987; 82: 1032–63p.

Isard M, Blake A. “Condensation - conditional density propagation for visual tracking. Int. J. Comput. Vision. 1998; 29(1): 5–28p.

Ziani D. “Feature Selection on Boolean Symbolic Objects.” Int J of Comput Sci and Info Technol (IJCSITY). 2013; 1(4).

Choi Y, Sharifahmadian E, Latifi S. “Performance analysis of contourlet-based hyperspectral image fusion methods.” Int J on Info Theory (IJIT). Oct. 2013; 2(1/2/3/4).


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/