Visual object tracking—classical and contemporary approaches

Springer Science and Business Media LLC - Tập 10 Số 1 - Trang 167-188 - 2016
Ahmad Ali1, Abdul Jalil1, Jianwei Niu2, Xinyi Zhao2, Saima Rathore1, Ali Javed3, Muhammad Aksam Iftikhar4
1Department of Computer and Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Islamabad, Pakistan
2State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China
3Department of Electrical (Telecom) Engineering, NUST Military College of Signals, Islamabad, Pakistan
4COMSATS Institute of Information Technology, Lahore, Pakistan

Tóm tắt

Từ khóa


Tài liệu tham khảo

Ta D N, ChenWC, Gelfand N, Pulli K. Surftrac: efficient tracking and continuous object recognition using local feature descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 2937–2944

Skrypnyk I, Lowe D G. Scene modelling, recognition and tracking with invariant image features. In: Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality. 2004, 110–119

Chau D P, Bremond F, Thonnat M. Object tracking in videos: approaches and issues. 2013, arXiv preprint arXiv: 1304.5212

Ko T. A survey on behavior analysis in video surveillance for homeland security applications. In: Proceedings of the 37th IEEE Applied Imagery Pattern Recognition Workshop. 2008, 1–8

Ess A, Schindler K, Leibe B, Van Gool L. Object detection and tracking for autonomous navigation in dynamic environments. The International Journal of Robotics Research, 2010, 29: 1707–1725

Mistry P, Maes P. SixthSense: a wearable gestural interface. In: Proceedings of ACM SIGGRAPH ASIA 2009 Sketches. 2009, 11

Bradski G R. Real time face and object tracking as a component of a perceptual user interface. In: Proceedings of the 4th IEEE Workshop on Applications of Computer Vision. 1998, 214–219

Zhu Z, Ji Q. Eye gaze tracking under natural head movements. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 918–923

Kim I, Choi H S, Yi K M, Choi J Y, Kong S G. Intelligent visual surveillance — a survey. International Journal of Control, Automation and Systems, 2010, 8(5): 926–939

Siemens S. Sistore CX EDS-intelligent video detection system. Technical Report. 2008

Collins R, Lipton A, Kanade T, Tolliver E. A system for video surveillance and monitoring. Technical Report CMU-RI-TR-00-12. 2000

Haritaoglu I, Harwood D, Davis L S. W4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809–830

Kettnaker V, Zabih R. Bayesian multi-camera surveillance. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 242–259

Hu W, Tan T, Wang L, Maybank S. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2004, 34(3): 334–352

Collins R T, Lipton A J, Fujiyoshi H, Kanade T. Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE, 2001, 89(10): 1456–1477

Greiffenhagen M, Comaniciu D, Niemann H, Ramesh V. Design, analysis, and engineering of video monitoring systems: an approach and a case study. Proceedings of the IEEE, 2001, 89(10): 1498–1517

Kumar R, Sawhney H, Samarasekera S, Hsu S, Tao H, Guo Y, Hanna K, Pope A, Wildes R, Hirvonen D, Hansen M, Burt P. Aerial video surveillance and exploitation. Proceedings of the IEEE, 2001, 89(10): 1518–1539

Coifman B, Beymer D, McLauchlan P, Malik J. A real-time computer vision system for vehicle tracking and traffic surveillance. Transportation Research Part C: Emerging Technologies, 1998, 6(4): 271–288

Tai J C, Tseng S T, Lin C P, Song K T. Real-time image tracking for automatic traffic monitoring and enforcement applications. Image and Vision Computing, 2004, 22(6): 485–501

Masoud O, Papanikolopoulos N P. A novel method for tracking and counting pedestrians in real-time using a single camera. IEEE Transactions on Vehicular Technology, 2001, 50(5): 1267–1278

Papanikolopoulos N P, Khosla P K. Adaptive robotic visual tracking: theory and experiments. IEEE Transactions on Automatic Control, 1993, 38(3): 429–445

Sakagami Y, Watanabe R, Aoyama C, Matsunaga S, Higaki N, Fujimura K. The intelligent asimo: system overview and integration. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2002, 2478–2483

Mondragon I F, Campoy P, Correa J F, Mejias L. Visual model feature tracking for UAV control. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing. 2007, 1–6

Lee J, Huang R, Vaughn A, Xiao X, Hedrick J K, Zennaro M, Sengupta R. Strategies of path-planning for a UAV to track a ground vehicle. In: Proceedings of Annual Autonomous Intelligent Networks and Systems Conference. 2003

Handmann U, Kalinkea T, Tzomakas C, Werner M, von Seelen W. Computer vision for driver assistance systems. In: Proceedings of Aerospace/Defense Sensing and Controls. 1998, 136–147

Avidan S. Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064–1072

Ahmed J, Shah M, Miller A, Harper D, Jafri M N. A vision-based system for a UGV to handle a road intersection. In: Proceedings of National Conference on Artificial Intelligence. 2007, 1077

Rand D, Kizony R, Weiss P T. The Sony playstation II eyetoy: low-cost virtual reality for use in rehabilitation. Journal of Neurologic Physical Therapy, 2008, 32(4): 153–163

Wang S, Xiong X, Xu Y, Wang C, Zhang W, Dai X, Zhang D. Facetracking as an augmented input in video games: enhancing presence, role-playing and control. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems. 2006, 1097–1106

Amini A A, Owen R L, Anandan P, Duncan J. Non-rigid motion models for tracking the left ventricular wall. In: Proceedings of the 12th International Conference on Information Processing in Medical Imaging. 1991, 343–357

Vasconcelos M J M, Ventura S M R, Freitas D R S, Tavares J M R S. Using statistical deformable models to reconstruct vocal tract shape from magnetic resonance images. Institution ofMechanical Engineers, Part H: Journal of Engineering in Medicine, 2010, 224(10): 1153–1163

Vasconcelos M J M, Ventura S M R, Freitas D R S, Tavares J M R S. Towards the automatic study of the vocal tract from magnetic resonance images. Journal of Voice: Official Journal of the Voice Foundation, 2011, 25: 732–742

Stauffer C, Grimson W E L. Learning patterns of activity using realtime tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747–757

Bodor R, Jackson M, Papanikolopoulos N. Vision-based human tracking and activity recognition. In: Proceedings of the 11thMediterranean Conference on Control and Automation. 2003, 18–20

Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. In: Proceedings of International Joint Conference on Artificial Intelligence. 1981, 674–679

Fitts J M. Precision correlation tracking via optimal weighting functions. In: Proceedings of the 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes. 1979, 280–283

Yilmaz A, Javed O, Shah M. Object tracking: a survey. ACM Computing Surveys, 2006, 38(4): 13

Joshi K A, Thakore D G. A survey on moving object detection and tracking in video surveillance system. International Journal of Soft Computing and Engineering, 2012: 2231–2307

Yang H, Shao L, Zheng F, Wang L, Song Z. Recent advances and trends in visual tracking: a review. Neurocomputing, 2011, 74(18): 3823–3831

Cannons K. A review of visual tracking. Technical Report CSE-2008-07. 2008

Geronimo D, Lopez A M, Sappa A D, Graf T. Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1239–1258

Ogale N A. A survey of techniques for human detection. Master’s Thesis. University of Maryland, 2006

Trucco E, Plakas K. Video tracking: a concise survey. IEEE Journal of Oceanic Engineering, 2006, 31(2): 520–529

Moeslund T B, Hilton A, Krüger V. A survey of advances in visionbased human motion capture and analysis. Computer Vision and Image Understanding, 2006, 104(2): 90–126

Aggarwal J K, Cai Q. Human motion analysis: a review. In: Proceedings of IEEE Nonrigid and Articulated Motion Workshop. 1997, 90–102

Kang W, Deng F. Research on intelligent visual surveillance for public security. In: Proceedings of IEEE/ACIS International Conference on Computer and Information Science. 2007, 824–829

Forsyth D A, Arikan O, Ikemoto L. Computational Studies of Human Motion: Tracking and Motion Synthesis. Boston: Now Publishers Inc., 2006

Zhan B, Monekosso D N, Remagnino P, Velastin S A, Xu L Q. Crowd analysis: a survey. Machine Vision and Applications, 2008, 19(5–6): 345–357

Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188

Jalal A S, Singh J. The state-of-the-art in visual object tracking. Informatica Slovenia, 2012, 36(3): 227–248

Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel A V D. A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 58

Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32–40

Cheng Y. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790–799

Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603–619

Comaniciu D, Meer P. Robust analysis of feature spaces: color image segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1997, 750–755

Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000, 142–149

Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564–575

Hero A O, Ma B, Michel O J J, Gorman J. Applications of entropic spanning graphs. IEEE Signal Processing Magazine, 2002, 19(5): 85–95

Yang C, Duraiswami R, Davis L. Efficient mean-shift tracking via a new similarity measure. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 176–183

Beleznai C, Fruhstuck B, Bischof H. Human tracking by fast mean shift mode seeking. Journal of Multimedia, 2006, 1(1): 1–8

Beleznai C, Fruhstuck B, Bischof H. Human tracking by mode seeking. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. 2005, 1–6

Beleznai C, Fruhstuck B, Bischof H. Tracking multiple humans by fast mean shift mode seeking. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. 2005, 25–32

Beleznai C, Fruhstuck B, Bischof H. Detecting humans in groups using a fast mean shift procedure. In: Proceedings of Workshop of the Austrian Association for Pattern Recogniton. 2004, 71–78

Beleznai C, Fruhstuck B, Bischof H. Human detection in groups using a fast mean shift procedure. In: Proceedings of International Conference on Image Processing. 2004, 349–352

Zivkovic Z, Krose B. An EM-like algorithm for color-histogram-based object tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 798–803

Zhou H, Yuan Y, Zhang Y, Shi C. Non-rigid object tracking in complex scenes. Pattern Recognition Letters, 2009, 30(2): 98–102

Ning J, Zhang L, Zhang D, Wu C. Robust object tracking using joint color-texture histogram. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23: 1245–1263

Shan C, Tan T, Wei Y. Real-time hand tracking using a mean shift embedded particle filter. Pattern Recognition, 2007, 40(7): 1958–1970

Wang X, Liu L, Tang Z. Infrared human tracking with improved mean shift algorithm based on multicue fusion. Journal of Applied Otics, 2009, 48(21): 4201–4212

Shen C, Brooks M J, Van Den Hengel A. Fast global kernel density mode seeking: applications to localization and tracking. IEEE Transactions on Image Processing, 2007, 16(5): 1457–1469

Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 798–805

Jeyakar J, Babu R V, Ramakrishnan K R. Robust object tracking with background-weighted local kernels. Computer Vision and Image Understanding, 2008, 112(3): 296–309

Khan M I, Ahmed J, Ali A, Masood A. Robust edge-enhanced fragment based normalized correlation tracking in cluttered and occluded imagery. In: Proceedings of Signal Processing, Image Processing and Pattern Recognition. 2009, 169–176

Kalman R E, Bucy R S. New results in linear filtering and prediction theory. Journal of Basic Engineering, 1961, 83: 95–108

Brookner E. Tracking and Kalman Filtering Made Easy. New York: Wiley, 1998

Grewal M S, Andrews A P. Kalman filtering: theory and practice using MATLAB. New York, Chichester, Brisbane: JohnWiley & Sons, 2008

Welch G, Bishop G. An introduction of the kalman filter. Technical Report. 2005

Asgarizadeh M, Pourghassem H. A robust object tracking synthetic structure using regional mutual information and edge correlation-based tracking algorithm in aerial surveillance application. Signal, Image and Video Processing, 2015, 9(1): 175–189

Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking. In: Proceedings of International Conference on Image Processing. 2000, 70–73

Li Z, Xu C, Li Y. Robust object tracking using mean shift and fast motion estimation. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing and Communication Systems. 2007, 734–737

Li X, Zhang T, Shen X, Sun J. Object tracking using an adaptive kalman filter combined with mean shift. Optical Engineering, 2010, 49(2): 020503

Ali A, Mirza S M. Object tracking using correlation, kalman filter and fast means shift algorithms. In: Proceedings of International Conference on Emerging Technologies. 2006, 174–178

Ahmed J, Jafri M N, Shah M, Akbar M. Real-time edge-enhanced dynamic correlation and predictive open-loop car-following control for robust tracking. Machine Vision and Applications, 2008, 19(1): 1–25

Boykov Y, Huttenlocher D P. Adaptive bayesian recognition in tracking rigid objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000, 697–704

Beymer D, McLauchlan P, Coifman B, Malik J. A real-time computer vision system for measuring traffic parameters. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1997, 495–501

Broida T J, Chellappa R. Estimation of object motion parameters from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(1): 90–99

Gennery D B. Visual tracking of known three-dimensional objects. International Journal of Computer Vision, 1992, 7(3): 243–270

Terzopoulos D, Szeliski R. Tracking with kalman snakes. In: Active Vision. Cambridge, MA, USA: MIT Press, 1993, 3–20

Blake A, Isard M. Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion. 1st ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1998

Cuevas E V, Zaldivar D, Rojas R. Kalman filter for vision tracking. Technical Report. 2005

Jang D S, Choi H I. Active models for tracking moving objects. Pattern Recognition, 2000, 33(7): 1135–1146

Ridder C, Munkelt O, Kirchner H. Adaptive background estimation and foreground detection using kalman-filtering. In: Proceedings of International Conference on recent Advances in Mechatronics. 1995, 193–199

Peterfreund N. Robust tracking of position and velocity with kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(6): 564–569

Anderson B D O, Moore J B. Optimal Filtering. Mincola: Courier Dover Publications, 2012

Doucet A, Godsill S, Andrieu C. On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 2000, 10(3): 197–208

Isard M, Blake A. Condensation–conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1): 5–28

Rao G M, Satyanarayana C. Visual object target tracking using particle filter: a survey. International Journal of Image, Graphics and Signal Processing, 2013, 5(6): 57–71

Duda R O, Hart P E. Pattern Classification and Scene Analysis. New York: Wiley, 1973

Gonzalez R C, Woods R E. Digital Image Processing. Upper Saddle River, N.J.: Pearson/Prentice Hall, 2008

Kuglin C D, Hines D C. The phase correlation image alignment method. IEEE Conference on Cybernetics and Society, 1975, 163–165

Lewis J P. Fast normalized cross-correlation. Vision Interface, 1995, 10(1): 120–123

Chien S I, Sung S H. Adaptive window method with sizing vectors for reliable correlation-based target tracking. Pattern Recognition, 2000, 33(2): 237–249

Manduchi R, Mian G A. Accuracy analysis for correlation-based image registration algorithms. In: Proceedings of IEEE International Symposium on Circuits and Systems. 1993, 834–837

Stone H S, Tao B, McGuire M. Analysis of image registration noise due to rotationally dependent aliasing. Journal of Visual Communication and Image Representation, 2003, 14(2): 114–135

Stone H S. Fourier-based image registration techniques. Technical Report. 2002

Foroosh H, Zerubia J B, Berthod M. Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing, 2002, 11(3): 188–200

Keller Y, Averbuch A, Miller O. Robust phase correlation. In: Proceedings of the 17th International Conference on Pattern Recognition. 2004, 740–743

Ahmed J, Jafri M N. Improved phase correlation matching. In: Proceedings of International Conference on Image and Signal Processing. 2008, 128–135

Blackman S S, Popoli R F. Design and Analysis of Modern Tracking Systems. Boston, M A: Artech House, 1999

Nixon M S, Aguado A S. Feature Extraction & Image Processing. London: Academic Press, 2008

Ali A, Jalil A, Ahmed J, Iftikhar M A, Hussain M. Correlation, kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking. Signal, Image and Video Processing, 2014: 1–19

Wren C R, Azarbayejani A, Darrell T, Pentland A P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780–785

Grimson W E L, Stauffer C, Romano R, Lee L. Using adaptive tracking to classify and monitor activities in a site. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1998, 22–29

Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999

KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. Video-Based Surveillance Systems. 2002, 135–144

Horprasert T, Harwood D, Davis L S. A robust background subtraction and shadow detection. In: Proceedings of Asian Conference on Computer Vision. 1999, 983–988

Horprasert T, Harwood D, Davis L S. A statistical approach for realtime robust background subtraction and shadow detection. In: Proceedings of International Conference on Computer Vision. 1999, 1–19

Oliver N M, Rosario B, Pentland A P. A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 831–843

Lipton A J, Fujiyoshi H, Patil R S. Moving target classification and tracking from real-time video. In: Proceedings of the 4th IEEE Workshop on Applications of Computer Vision. 1998, 8–14

Dailey D J, Cathey F W, Pumrin S. An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Transactions on Intelligent Transportation Systems, 2000, 1(2): 98–107

Dailey D J, Li L. An algorithm to estimate vehicle speed using uncalibrated cameras. In: Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems. 1999, 441–446

Horn B K P, Schunck B G. Determining optical flow. Technical Report. 1980

Black M J, Anandan P. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 1996, 63(1): 75–104

Szeliski R, Coughlan J. Spline-based image registration. International Journal of Computer Vision, 1997, 22(3): 199–218

Shi J, Tomasi C. Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1994, 593–600

Rangarajan K, Shah M. Establishing motion correspondence. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1991, 103–108

Papageorgiou C P, Oren M, Poggio T. A general framework for object detection. In: Proceedings of the 6th IEEE International Conference on Computer Vision. 1998, 555–562

Cremers D, Schnorr C. Statistical shape knowledge in variational motion segmentation. Image and Vision Computing, 2003, 21(1): 77–86

Li B, Chellappa R, Zheng Q, Der S Z. Model-based temporal object verification using video. IEEE Transactions on Image Processing, 2001, 10(6): 897–908

Bertalmio M, Sapiro G, Randall G. Morphing active contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(7): 733–737

Mansouri A R. Region tracking via level set PDEs without motion computation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 947–961

Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619–1632

Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. In: Proceedings of British Machine Vision Conference. 2006, 1(5): 6

Collins R T, Liu Y, Leordeanu M. Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631–1643

Santner J, Leistner C, Saffari A, Pock T, Bischof H. Prost: parallel robust online simple tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 723–730

Liu X, Yu T. Gradient feature selection for online boosting. In: Proceedings of the 11th IEEE International Conference on Computer Vision. 2007, 1–8

Avidan S. Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261–271

Wang J, Chen X, Gao W. Online selecting discriminative tracking features using particle filter. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 1037–1042

Kuncheva L I. Combining pattern classifiers: methods and algorithms. IEEE Transactions on Neural Networks, 2007, 18(3): 964–964

Bishop C M. Pattern Recognition and Machine Learning. Springer, 2006

Hare S, Saffari A, Torr P H S. Struck: structured output tracking with kernels. In: Proceedings of IEEE International Conference on Computer Vision. Nov 2011, 263–270

Stalder S, Grabner H. On-line Boosting Trackers. ETH-Zurich, 2009

Grabner H, Leistner C, Bischof H. Semi-supervised on-line boosting for robust tracking. In: Proceedings of European Conference on Computer Vision. 2008, 234–247

Zeisl B, Leistner C, Saffari A, Bischof H. On-line semi-supervised multiple-instance boosting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1879–1879

Saffari A, Leistner C, Godec M, Bischof H. Robust multi-view boosting with priors. In: Proceedings of European Conference on Computer Vision, 2010, 776–789

Leistner C, Saffari A, Roth P M, Bischof H. On robustness of on-line boosting—a competitive study. In: Proceedings of IEEE International Conference on Computer Vision Workshops. 2009, 1362–1369

Masnadi-Shirazi H, Mahadevan V, Vasconcelos N. On the design of robust classifiers for computer vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 779–786

Zhang K, Song H. Real-time visual tracking via online weighted multiple instance learning. Pattern Recognition, 2013, 46(1): 397–411

Williams O, Blake A, Cipolla R. A sparse probabilistic learning algorithm for real-time tracking. In: Proceedings of IEEE International Conference on Computer Vision. 2003, 353–360

Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. 1995, 1942–1948

Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium onMicroMachine and Human Science. 1995, 39–43

Poli R. Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications, 2008, 2008: 3

Clerc M, Kennedy J. The particle swarm — explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58–73

Wachowiak M P, Smolikova R, Zheng Y, Zurada J M, Elmaghraby A S. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 289–301

Engelbrecht A P. Computational Intelligence: an Introduction. 2nd ed. New York: John Wiley & Sons, 2007

Sedighizadeh D, Masehian E. Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 2009, 1(5): 486–502

Zhang X, Hu W, Maybank S, Zhu M. Sequential particle swarm optimization for visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1–8

Zhang X, Hu W, Qu W, Maybank S. Multiple object tracking via species-based particle swarm optimization. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(11): 1590–1602

Akbari R, Jazi M D, Palhang M. A hybrid method for robust multiple objects tracking in cluttered background. In: Proceedings of the 2nd International Conference on Information & Communication Technologies. 2006, 1562–1567

Kwolek B. Multi-object tracking using particle swarm optimization on target interactions. In: Proceedings of Advances in Heuristic Signal Processing and Applications. 2013, 63–78

Anton-Canalis L, Hernandez-Tejera M, Sanchez-Nielsen E. Particle swarms as video sequence inhabitants for object tracking in computer vision. In: Proceedings of the 6th International Conference on Intelligent Systems Design and Applications. 2006, 604–609

Zheng Y, Meng Y. Adaptive object tracking using particle swarm optimization. In: Proceedings of International Symposium on Computational Intelligence in Robotics and Automation. 2007, 43–48

Tawab A M A, Abdelhalim M B, Habib S E D. Efficient multi-feature PSO for fast gray level object-tracking. Applied Soft Computing, 2014, 14: 317–337

Borra S K, Chaparala S K. Tracking of an object in video stream using a hybrid PSO-FCM and pattern matching. International Journal of Engineering Research and Technology, 2013, 2

Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306

Candes E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207–1223

Wright J, Ma Y, Mairal J, Sapiro G, Huang T S, Yan S. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 2010, 98(6): 1031–1044

Sapiro G, Mairal J, Wright J, Ma Y, Huang T, Yan S. Sparse representation for computer vision and pattern recognition. Technical Report. 2009

Yang J, Wright J, Huang T S, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861–2873

Wright J, Yang A Y, Ganesh A, Sastry S S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227

Mei X, Ling H. Robust visual tracking using l1 minimization. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1436–1443

Mei X. Visual tracking and illumination recovery via sparse representation. Dissertation for the Doctoral Degree. University of Maryland, 2009

Mei X, Ling H. Robust visual tracking and vehicle classification via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259–2272

Liu B, Yang L, Huang J, Meer P, Gong L, Kulikowski C. Robust and fast collaborative tracking with two stage sparse optimization. In: Proceedings of European Conference on Computer Vision. 2010, 624–637

Liu J, Huang J, Yang L, Kulikowski C. Robust tracking using local sparse appearance model and k-selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011, 1313–1320

Zhong W, Lu H, Yang H M. Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1838–1845

Jia X, Lu X, Yang M H. Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829

Zhang K, Zhang L, Yang M H. Real-time compressive tracking. In: Proceedings of European Conference on Computer Vision. 2012, 864–877

Zhang S, Yao H, Sun X, Lu X. Sparse coding based visual tracking: review and experimental comparison. Pattern Recognition, 2013, 46(7): 1772–1788

Oliva A, Torralba A. The role of context in object recognition. Trends in Cognitive Sciences, 2007, 11(12): 520–527

Divvala S K, Hoiem D, Hays J H, Efros A A, Hebert M. An empirical study of context in object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1271–1278

Yang M, Wu Y, Hua G. Context-aware visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(7): 1195–1209

Li Y, Nevatia R. Key object driven multi-category object recognition, localization and tracking using spatio-temporal context. In: Proceedings of Europian Conference on Computer Vision. 2008, 409–422

Nguyen H T, Ji Q, Smeulders A W M. Spatio-temporal context for robust multitarget tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 52–64

Wen L, Cai Z, Lei Z, Yi D, Li S. Robust online learned spatio-temporal context model for visual tracking. IEEE Transactions on Image Processing, 2014, 23(2): 785–796

Grabner H, Matas J, Van Gool L, Cattin P. Tracking the invisible: Learning where the object might be. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1285–1292

Wu Z, Hristov N I, Hedrick T L, Kunz T H, Betke M. Tracking a large number of objects from multiple views. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1546–1553

Sugimura D, Kitani KM, Okabe T, Sato Y, Sugimoto A. Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1467–1474

Ali S, Shah M. Floor fields for tracking in high density crowd scenes. Lecture Notes in Computer Science. 2008, 5303: 1–14

Zhao T, Nevatia R. Tracking multiple humans in crowded environment. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 406–413

Betke M, Hirsh D E, Bagchi A, Hristov N I, Makris N C, Kunz T H. Tracking large variable numbers of objects in clutter. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2007, 1–8

Li Y, Huang C, Nevatia R. Learning to associate: Hybridboosted multitarget tracker for crowded scene. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 2953–2960

Wu B, Nevatia R. Tracking of multiple, partially occluded humans based on static body part detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 951–958

Brostow G J, Cipolla R. Unsupervised Bayesian detection of independent motion in crowds. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 594–601

Pellegrini S, Ess A, Schindler K, Van Gool L. You’ll never walk alone: Modeling social behavior for multi-target tracking. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 261–268

Rodriguez M, Ali S, Kanade T. Tracking in unstructured crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1389–1396

Kratz L, Nishino K. Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 693–700

Rodriguez M, Sivic J, Laptev I, Audibert J Y. Data-driven crowd analysis in videos. In: Proceedings of IEEE International Conference on Computer Vision. 2011, 1235–1242

Idrees H, Warner N, Shah M. Tracking in dense crowds using prominence and neighborhood motion concurrence. Image and Vision Computing, 2014, 32(1): 14–26

Zhang L, Maaten L. Structure preserving object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 1838–1845

Zhu F, Wang X, Yu N. Crowd tracking with dynamic evolution of group structures. In: Proceedings of the 13th European Conference on Computer Vision–ECCV. 2014, 139–154

Gao Y, Ji R, Zhang L, Hauptmann A. Symbiotic tracker ensemble towards a unified tracking framework. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(7): 1122–1131

Zhong B, Yao H, Chen S, Ji R, Chin T J, Wang H. Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition, 2014, 47(3): 1395–1410

Yao A, Lin X, Wang G, Yu S. A compact association of particle filtering and kernel based object tracking. Pattern Recognition, 2012, 45(7): 2584–2597

Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision—ECCV 2012. 2012, 702–715

Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2013, 2411–2418

Ross D A, Lim J, Lin R S, Yang M H. Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008, 77(1–3): 125–141

Kwon J, Lee K M. Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1269–1276

Wang Y, Qi Y, Li Y. Memory-based multiagent coevolution modeling for robust moving object tracking. The Scientific World Journal, 2013, 2013

Wang Y, Qi Y. Memory-based cognitive modeling for robust object extraction and tracking. Applied Intelligence, 2013, 39(3): 614–629

Smith K, Ba S O, Odobez J M, Gatica-Perez D. Tracking the visual focus of attention for a varying number of wandering people. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(7): 1212–1229