Laser-based tracking of multiple interacting pedestrians via on-line learning

Abstract Successful multi-target tracking requires locating the targets and labeling their identities. For a laser-based tracking system, this mission becomes significantly challenging when the targets are in close proximity or frequently interact with one another. This paper presents a novel on-line learning-based method for laser-based multi-target tracking. When the targets do not interact with one another, multiple independent trackers are employed to train a classifier for each target. When the targets are in close proximity, the learned classifiers are used to assist in tracking. The tracking and learning supplement each other in the proposed method, which is helpful in dealing with difficult problems encountered in laser-based multi-target tracking; moreover, it ensures that the entire process can be completely automatic and available on-line. Various evaluations have demonstrated that this method performs better than previous methods when interactions occur, and it can maintain the correct tracking under various complex tracking situations.

[1]  François Brémond,et al.  Online learning neural tracker , 2011, Neurocomputing.

[2]  Andrew Blake,et al.  Sparse Bayesian learning for efficient visual tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  SongXuan,et al.  An online system for multiple interacting targets tracking , 2013 .

[4]  Ryosuke Shibasaki,et al.  Detection and tracking of multiple pedestrians by using laser range scanners , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .

[6]  Anamitra Makur,et al.  Online adaptive radial basis function networks for robust object tracking , 2010, Comput. Vis. Image Underst..

[7]  Xuan Song,et al.  Fusion of laser and vision for multiple targets tracking via on-line learning , 2010, 2010 IEEE International Conference on Robotics and Automation.

[8]  Xuan Song,et al.  An online system for multiple interacting targets tracking: Fusion of laser and vision, tracking and learning , 2013, TIST.

[9]  Leonidas J. Guibas,et al.  Fourier Theoretic Probabilistic Inference over Permutations , 2009, J. Mach. Learn. Res..

[10]  Sanjeev R. Kulkarni,et al.  Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations , 2005, NIPS.

[11]  Xuan Song,et al.  An online approach: Learning-Semantic-Scene-by-Tracking and Tracking-by-Learning-Semantic-Scene , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Ramakant Nevatia,et al.  Tracking multiple humans in complex situations , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Xuan Song,et al.  Tracking interacting targets with laser scanner via on-line supervised learning , 2008, 2008 IEEE International Conference on Robotics and Automation.

[14]  Ramakant Nevatia,et al.  Multi-target tracking by on-line learned discriminative appearance models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, CVPR.

[17]  Patrick Pérez,et al.  Maintaining multimodality through mixture tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Ying Wu,et al.  Closed-Loop Adaptation for Robust Tracking , 2010, ECCV.

[19]  Maja J. Mataric,et al.  A laser-based people tracker , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[20]  O. Sebakhy,et al.  State regulation in linear discrete-time systems in minimum time , 1979 .

[21]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Ryosuke Shibasaki,et al.  Multi-modal tracking of people using laser scanners and video camera , 2008, Image Vis. Comput..

[23]  Xuan Song,et al.  Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning , 2008, ECCV.

[24]  Shai Avidan,et al.  Support Vector Tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[25]  Frank Dellaert,et al.  MCMC-based particle filtering for tracking a variable number of interacting targets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Roberto Manduchi,et al.  Hybrid joint-separable multibody tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Rama Chellappa,et al.  Estimation of Object Motion Parameters from Noisy Images , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Frank Dellaert,et al.  MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Gregory D. Hager,et al.  Dynamic Foreground/Background Extraction from Images and Videos using Random Patches , 2006, NIPS.

[30]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[31]  KhanZia,et al.  MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets , 2005 .

[32]  Wolfram Burgard,et al.  Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities , 2008, 2008 IEEE International Conference on Robotics and Automation.

[33]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[34]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[35]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  Gérard G. Medioni,et al.  Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Wang Xing-Yuan,et al.  Chaotic diagonal recurrent neural network , 2012 .

[39]  Dan Schonfeld,et al.  Real-time interactively distributed multi-object tracking using a magnetic-inertia potential model , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[40]  Yuan Li,et al.  Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Complex Visual Objects , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Ying Wu,et al.  Decentralized multiple target tracking using netted collaborative autonomous trackers , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[43]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[44]  James J. Little,et al.  Robust Visual Tracking for Multiple Targets , 2006, ECCV.

[45]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[46]  Stefan Carlsson,et al.  Multi-Target Tracking - Linking Identities using Bayesian Network Inference , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[47]  王兴元,et al.  Fuzzy neural network based on a Sigmoid chaotic neuron , 2012 .

[48]  Stefan Carlsson,et al.  Tracking and Labelling of Interacting Multiple Targets , 2006, ECCV.

[49]  Wolfram Burgard,et al.  People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters , 2003, Int. J. Robotics Res..

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

[51]  Z. M. Hefed.  Object tracking , 1999 .

[52]  Wolfram Burgard,et al.  Using Boosted Features for the Detection of People in 2D Range Data , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[53]  Anamitra Makur,et al.  Robust Object Tracking with Radial Basis Function Networks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.