A Comparison of Multi Hypothesis Kalman Filter and Particle Filter for Multi-target Tracking

Visual tracking of multiple targets is a key step in surveillance scenarios, far from being solved due to its intrinsic ill-posed nature. In this paper, a comparison of MultiHypothesis Kalman Filter and Particle Filter-based tracking is presented. Both methods receive input from a novel online background subtraction algorithm. The aim of this work is to highlight advantages and disadvantages of such tracking techniques. Results are performed using public challenging data set (PETS 2009), in order to evaluate the approaches on significant benchmark data.

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

[2]  Mubarak Shah,et al.  Automated Multi-Camera Surveillance: Algorithms and Practice , 2008, The International Series in Video Computing.

[3]  Oswald Lanz,et al.  Approximate Bayesian multibody tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.

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

[6]  Jean-Marc Odobez,et al.  Evaluating Multi-Object Tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[7]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[8]  Sidney S. Fels,et al.  Evaluation of Background Subtraction Algorithms with Post-Processing , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[9]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  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.

[11]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Multiple and Compound Visual Objects , 2000 .

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

[13]  Simone Calderara,et al.  Reliable background suppression for complex scenes , 2006, VSSN '06.

[14]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[15]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[16]  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).

[17]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[18]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[19]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

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

[22]  Y. Bar-Shalom,et al.  Tracking in clutter with nearest neighbor filters: analysis and performance , 1996, IEEE Transactions on Aerospace and Electronic Systems.