A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm

Display Omitted Using a meta-heuristic approach to decrease required particles in particle filter.Customizing meta-heuristic technique for tracking.Proposing a simple appearance method to address occlusion. An important problem in tracking methods is how to manage the changes in object appearance, such as illumination changes, partial/full occlusion, scale, and pose variation during the tracking process. In this paper, we propose an occlusion free object tracking method together with a simple adaptive appearance model. The proposed appearance model which is updated at the end of each time step includes three components: the first component consists of a fixed template of target object, the second component shows rapid changes in object appearance, and the third one maintains slow changes generated along the object path. The proposed tracking method not only can detect occlusion and handle it, but also it is robust against changes in the object appearance model. It is based on particle filter which is a robust technique in tracking and handles non-linear and non-Gaussian problems. We have also employed a meta-heuristic approach that is called Modified Galaxy based Search Algorithm (MGbSA), to reinforce finding the optimum state in the particle filter state space. The proposed method was applied to some benchmark videos and its results were satisfactory and better than results of related works.

[1]  Shiuh-Ku Weng,et al.  Video object tracking using adaptive Kalman filter , 2006, J. Vis. Commun. Image Represent..

[2]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[3]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Majid Sarrafzadeh,et al.  Collaborative and Reconfigurable Object Tracking , 2005, International Conference on Field Programmable Logic and Applications, 2005..

[5]  Jean-Christophe Nebel,et al.  Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Ebrahimi MoghaddamMohsen,et al.  A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm , 2017 .

[7]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Yoji Yamada,et al.  An HMM-based temporal difference learning with model-updating capability for visual tracking of human communicational behaviors , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[9]  Jin Young Choi,et al.  Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking , 2011, Comput. Vis. Image Underst..

[10]  Renato A. Krohling,et al.  Particle Filter with Differential Evolution for Trajectory Tracking , 2011 .

[11]  BlakeAndrew,et al.  C ONDENSATION Conditional Density Propagation forVisual Tracking , 1998 .

[12]  Dit-Yan Yeung,et al.  Learning a Deep Compact Image Representation for Visual Tracking , 2013, NIPS.

[13]  Emilio Maggio,et al.  Hybrid particle filter and mean shift tracker with adaptive transition model , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[14]  Ahmed Abdel-Hadi.  Real-time object tracking using color-based Kalman particle filter , 2010, The 2010 International Conference on Computer Engineering & Systems.

[15]  Jing Zhao,et al.  Particle filter based on Particle Swarm Optimization resampling for vision tracking , 2010, Expert Syst. Appl..

[16]  Bo Zhang,et al.  Robust appearance-guided particle filter for object tracking with occlusion analysis , 2008 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Liyuan Li,et al.  Integrating intensity and texture differences for robust change detection , 2002, IEEE Trans. Image Process..

[19]  Shah-HosseiniHamed.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011 .

[20]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[21]  Fatih Porikli,et al.  Performance Evaluation of Object Detection and Tracking Systems , 2006 .

[22]  Glenn Healey,et al.  Segmenting images using normalized color , 1992, IEEE Trans. Syst. Man Cybern..

[23]  Ngai Ming Kwok,et al.  Evolutionary particle filter: re-sampling from the genetic algorithm perspective , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Nando de Freitas,et al.  Toward Practical N2 Monte Carlo: the Marginal Particle Filter , 2005, UAI.

[25]  Hamed Shah-Hosseini,et al.  Otsu's criterion-based multilevel thresholding by a nature-inspired metaheuristic called Galaxy-based Search Algorithm , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[26]  T. Poggio,et al.  A Contour-Based Moving Object Detection and Tracking , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[27]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[28]  Kyoung Mu Lee,et al.  Visual tracking via geometric particle filtering on the affine group with optimal importance functions , 2009, CVPR.

[29]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[30]  Zhi-Qiang Liu,et al.  Tracking Human Hand Motion Using Genetic Particle Filter , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[31]  Lakhmi C. Jain,et al.  Human Tracking: A State-of-Art Survey , 2010, KES.

[32]  Chaur-Chin Chen,et al.  Color images' segmentation using scale space filter and markov random field , 1992, Pattern Recognit..

[33]  Xiaoqin Zhang,et al.  Sequential particle swarm optimization for visual tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Jin Zhang,et al.  Multi-cue-based CamShift guided particle filter tracking , 2011, Expert Syst. Appl..

[35]  Hamed Shah-Hosseini,et al.  Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation , 2011, Int. J. Comput. Sci. Eng..

[36]  Kentaro Yokoi,et al.  Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements , 2010, MVA.