Dynamic tracking re-adjustment: a method for automatic tracking recovery in complex visual environments

Detection and analysis of events from video sequences is probably one of the most important research issues in computer vision and pattern analysis society. Before, however, applying methods and tools for analyzing actions, behavior or events, we need to implement robust and reliable tracking algorithms able to automatically monitor the movements of many objects in the scene regardless of the complexity of the background, existence of occlusions and illumination changes. Despite the recent research efforts in the field of object tracking, the main limitation of most of the existing algorithms is that they are not enriched with automatic recovery strategies able to re-initialize tracking whenever its performance severely deteriorates. This is addressed in this paper by proposing an automatic tracking recovery tool which improves the performance of any tracking algorithm whenever the results are not acceptable. For the recovery, non-linear object modeling tools are used which probabilistically label image regions to object classes. The models are also time varying. The first property is implemented in our case using concepts from functional analysis which allow parametrization of any arbitrary non-linear function (with some restrictions on its continuity) as a finite series of known functional components but of unknown coefficients. The second property is addressed by proposing an innovative algorithm that optimally estimates the non-linear model at an upcoming time instance based on the current non-linear models that have been already approximated. The architecture is enhanced by a decision mechanism which permits verification of the time instances in which tracking recovery should take place. Experimental results on a set of different video sequences that present complex visual phenomena (full and partial occlusions, illumination variations, complex background, etc) are depicted to demonstrate the efficiency of the proposed scheme in proving tracking in very difficult visual content conditions. Additionally, criteria are proposed to objectively evaluate the tracking performance and compare it with other strategies.

[1]  Satoshi Yonemoto,et al.  Multitarget tracking using mean-shift with particle filter based initialization , 2008, 2008 12th International Conference Information Visualisation.

[2]  Demin Wang.  Unsupervised video segmentation based on watersheds and temporal tracking , 1998, IEEE Trans. Circuits Syst. Video Technol..

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

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

[5]  Jorge S. Marques,et al.  Performance evaluation of object detection algorithms for video surveillance , 2006, IEEE Transactions on Multimedia.

[6]  Datong Chen,et al.  Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Pascal Fua,et al.  Texture Boundary Detection for Real-Time Tracking , 2004, ECCV.

[8]  Theodora A. Varvarigou,et al.  An architecture for a self configurable video supervision , 2008, AREA '08.

[9]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[10]  David Suter,et al.  Adaptive Object Tracking Based on an Effective Appearance Filter , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Henry Medeiros,et al.  Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks , 2008, IEEE Journal of Selected Topics in Signal Processing.

[13]  Anil K. Jain,et al.  Object tracking using deformable templates , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[14]  Jerry L. Prince,et al.  Image registration based on boundary mapping , 1996, IEEE Trans. Medical Imaging.

[15]  Joachim M. Buhmann,et al.  Topology Free Hidden Markov Models: Application to Background Modeling , 2001, ICCV.

[16]  Ulrich Kressel,et al.  Tracking non-rigid, moving objects based on color cluster flow , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Ehud Rivlin,et al.  Tracking by Affine Kernel Transformations Using Color and Boundary Cues , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[19]  Qiang Ji,et al.  Multi-View Face Tracking with Factorial and Switching HMM , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[20]  Stefanos D. Kollias,et al.  An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture , 2003, IEEE Trans. Neural Networks.

[21]  G. J. Smith,et al.  Behind the Screens: Examining Constructions of Deviance and Informal Practices among CCTV Control Room Operators in the UK , 2002 .

[22]  Max Mignotte,et al.  Statistical background subtraction using spatial cues , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

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

[24]  A. Murat Tekalp,et al.  Digital Video Processing , 1995 .

[25]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[26]  Alexander H. Waibel,et al.  A real-time face tracker , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[27]  Luc Van Gool,et al.  First ACM international workshop on analysis and retrieval of events, actions and workflows in video streams , 2008, ACM Multimedia.

[28]  E. Kreyszig.  Introductory Functional Analysis With Applications , 1978 .

[29]  J. Odobez,et al.  Embedding Motion in Model-Based Stochastic Tracking , 2004, IEEE Transactions on Image Processing.

[30]  Xin Li,et al.  Contour-based object tracking with occlusion handling in video acquired using mobile cameras , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Luc Van Gool,et al.  Proceedings of the 1st ACM Workshop on Analysis and Retrieval of Events/Actions and Workflows in Video Streams, AREA 2008, Vancouver, British Columbia, Canada, October 31, 2008 , 2008, AREA.

[32]  R. Venkatesh Babu,et al.  Robust object tracking with background-weighted local kernels , 2008, Comput. Vis. Image Underst..

[33]  Du-Ming Tsai,et al.  Independent Component Analysis-Based Background Subtraction for Indoor Surveillance , 2009, IEEE Transactions on Image Processing.

[34]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[35]  Theodora A. Varvarigou,et al.  Polymnia: Personalised Leisure and Entertainment over Cross Media Intelligent Platforms , 2004, EWIMT.

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

[37]  Luc Van Gool,et al.  Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  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).