Occlusion management strategies for pedestrians tracking across fisheye camera networks

This paper presents a particle filtering algorithm for multiple pedestrian objects tracking across fisheye cameras network. For this purpose, we first suggest a particle filter tracking algorithm that integrates a best view selection strategy to ensure tracking consistency across multiple cameras. The proposed best view selection strategy, based on a model describing the known static occluders of the scene, favors the view in which the static occlusion suffered by a partially occluded pedestrian object is the least severe. We propose to estimate the severity of a partial occlusion through a new metric, called visibility index. Secondly, to perform multi-pedestrian object tracking, we propose a framework in which multiple autonomous instances of the proposed particle filter are used, each filter tracking one specific pedestrian object and taking dynamic occlusions into account. The efficiency of the proposed distributed framework lies in the fact that it performs tracking in linear complexity in terms of the number of tracked objects.

[1]  Justus H. Piater,et al.  Multi-view Object Tracking Using Sequential Belief Propagation , 2006, ACCV.

[2]  S. Spinsante,et al.  Proposal of a driver assistance system based on video and radar data fusion , 2008, 2008 16th International Conference on Software, Telecommunications and Computer Networks.

[3]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[4]  Frank Dellaert,et al.  An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets , 2004, ECCV.

[5]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[6]  Yoichi Sato,et al.  3D Head Tracking using the Particle Filter with Cascaded Classifiers , 2006, BMVC.

[7]  Andrea Cavallaro,et al.  Multi-Camera Networks: Principles and Applications , 2009 .

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

[9]  Irene Y. H. Gu,et al.  Bayesian online learning on Riemannian manifolds using a dual model with applications to video object tracking , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[10]  Sidney S. Fels,et al.  A Multi-Camera Surveillance System that Estimates Quality-of-View Measurement , 2007, 2007 IEEE International Conference on Image Processing.

[11]  Justus H. Piater,et al.  Multi-camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration , 2007, ACCV.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Juho Kannala,et al.  A generic camera calibration method for fish-eye lenses , 2004, ICPR 2004.

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  Ulrich Hofmann,et al.  Fusion of occupancy grid mapping and model based object tracking for driver assistance systems using laser and radar sensors , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  Shahriar B. Shokouhi,et al.  Human Tracking Using Spatialized Multi-level Histogram and Mean Shift , 2010, 2010 Canadian Conference on Computer and Robot Vision.

[17]  Rama Chellappa,et al.  Object Detection, Tracking and Recognition for Multiple Smart Cameras , 2008, Proceedings of the IEEE.

[18]  Hichem Snoussi,et al.  Robust detection and tracking pedestrian object for real time surveillance applications , 2011, International Conference on Graphic and Image Processing.

[19]  Masafumi Hashimoto,et al.  People detection and tracking from fish-eye image based on probabilistic appearance model , 2011, SICE Annual Conference 2011.

[20]  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).

[21]  Luc Van Gool,et al.  Color-Based Object Tracking in Multi-camera Environments , 2003, DAGM-Symposium.

[22]  Stanley Richardson.  Driver distraction and inattention, advances in research and countermeasures , 2013 .

[23]  Paul M. Salmon,et al.  Examining the relationship between driver distraction and driving errors: a discussion of theory, studies and methods , 2012 .

[24]  M.M. Trivedi,et al.  Visual context capture and analysis for driver attention monitoring , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[25]  Dimitrios Makris,et al.  Performance evaluation of object tracking algorithms , 2007 .

[26]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[27]  Cina Motamed,et al.  Visibility management for object tracking in the context of a fisheye camera network , 2012, 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC).

[28]  Hichem Snoussi,et al.  Improved mean shift integrating texture and color features for robust real time object tracking , 2012, The Visual Computer.

[29]  Reinhard Koch,et al.  Robust GPU-assisted camera tracking using free-form surface models , 2007, Journal of Real-Time Image Processing.