Multiple and variable target visual tracking for video-surveillance applications

Visual detection and tracking are interdisciplinary tasks which are oriented at estimating the state of one or multiple moving objects in a video sequence. This is one of the first tasks in processing video systems which try to describe human behaviour in different contexts, such as video-surveillance, sport technique analysis. This work presents a multiple object tracking system which properly hybridizes particle filters and memetic algorithms to produce a more reliable and efficient tracking algorithm. The system has been tested on synthetic and real image sequences, with the aim of describing their performance for different levels of noise, occlusions, a variable number of objects, etc. Experimental results demonstrate that the proposed system accurately tracks multiple objects in the scene, by grouping and ungrouping them when necessary, while keeping their identities during the sequence of images. Moreover, the performance of the proposed system is not strongly affected by the increase in the number of objects, maintaining computational load and precision in proper balance.

[1]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

[2]  P. Fearnhead,et al.  Building Robust Simulation-based Filters for Evolving Data Sets , 2007 .

[3]  Claudio Rossi,et al.  Tracking Moving Optima Using Kalman-Based Predictions , 2008, Evolutionary Computation.

[4]  J. Huang,et al.  Curse of dimensionality and particle filters , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[5]  David Suter,et al.  A consensus-based method for tracking: Modelling background scenario and foreground appearance , 2007, Pattern Recognit..

[6]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[7]  Simon J. Godsill,et al.  Tracking variable number of targets using sequential monte carlo methods , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[8]  Fang-Hsuan Cheng,et al.  Real time multiple objects tracking and identification based on discrete wavelet transform , 2006, Pattern Recognit..

[9]  P. Bickel,et al.  Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems , 2008, 0805.3034.

[10]  Mubarak Shah,et al.  Modeling inter-camera space-time and appearance relationships for tracking across non-overlapping views , 2008, Comput. Vis. Image Underst..

[11]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[12]  Pablo Moscato,et al.  A Gentle Introduction to Memetic Algorithms , 2003, Handbook of Metaheuristics.

[13]  Xiaojun Wu,et al.  Model based human motion tracking using probability evolutionary algorithm , 2008, Pattern Recognit. Lett..

[14]  Ming Xu,et al.  Tracking the soccer ball using multiple fixed cameras , 2009, Comput. Vis. Image Underst..

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Jia Liu,et al.  Automatic Player Detection, Labeling and Tracking in Broadcast Soccer Video , 2007, BMVC.

[17]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Juan José Pantrigo,et al.  Hybridizing particle filters and population-based metaheuristics for dynamic optimization problems , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[19]  Juan José Pantrigo,et al.  Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms , 2011, Expert Syst. J. Knowl. Eng..

[20]  Andrew Gilbert,et al.  Incremental, scalable tracking of objects inter camera , 2008, Comput. Vis. Image Underst..

[21]  Jouko Lampinen,et al.  Rao-Blackwellized particle filter for multiple target tracking , 2007, Inf. Fusion.

[22]  William E. Hart,et al.  Recent Advances in Memetic Algorithms , 2008 .

[23]  Matej Kristan,et al.  Closed-world tracking of multiple interacting targets for indoor-sports applications , 2009, Comput. Vis. Image Underst..

[24]  Juan José Pantrigo,et al.  Representation spaces in a visual-based human action recognition system , 2009, Neurocomputing.

[25]  Daniel Vanderpooten,et al.  A bi-criteria approach for the data association problem , 2006, Ann. Oper. Res..

[26]  Alberto Del Bimbo,et al.  Adaptive uncertainty estimation for particle filter-based trackers , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[27]  M. Gokmen,et al.  Integrating Differential Evolution and Condensation Algorithms for License Plate Tracking , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[28]  Lin Zhu,et al.  Tracking multiple objects through occlusion with online sampling and position estimation , 2008, Pattern Recognit..

[29]  Gregory D. Hager,et al.  Probabilistic data association methods in visual tracking of groups , 2004, CVPR 2004.

[30]  Rafael Muñoz-Salinas,et al.  A multiple object tracking approach that combines colour and depth information using a confidence measure , 2008, Pattern Recognit. Lett..

[31]  Larry S. Davis,et al.  Joint Audio-Visual Tracking Using Particle Filters , 2002, EURASIP J. Adv. Signal Process..