Reseach Projects- Video Tracking


Grant Awards

Research Projects



A Motion-Based Particle Filter and Randomly Perturbed Active Contours for Object

Tracking Applications

    Many real-world applications require accurate object tracking. Traditional applications include video surveillance, autonomous vehicle navigation, human computer interfaces, robot localization, etc. Recent advances in computer, multimedia and communication technologies have created opportunities in new applications such as wireless communication, interactive imaging and virtual reality. It is also a very challenging task in that target’s state space representation can be highly non-linear and the observations (e.g., audio and/or visual sensory data) are almost always corrupted by background clutters.

    In a probabilistic framework, the tracking problem is formulated as the estimation of the posterior density of the target given all past observations. Since the target’s dynamics and observations can be highly non-linear and non-Gaussian, Kalman filter fails to track objects in real-world environment. Sequential Monte Carlo filters, also known as Particle filters, use a non-parametric technique and a set of random samples, also called particles, to estimate the posterior density. A proposal density is used to easily generate the samples. Each sample is assigned a proper weight to make up the difference between the posterior and the proposal densities. Theoretically, if the number of samples is sufficiently large, the sample approximation of the posterior distribution can be made arbitrarily accurate. Practically, only a finite number of samples can be used. Moreover, when a good dynamic model of the target is not available, Particle filter samples regions of the conditional density function that are not in the vicinity of modes associated with the tracked object. Consequently, the tracker looses sight of the tracked object and attempts to monitor spurious objects and clutter.

    To overcome the above difficulties, we proposed to introduce motion cues in the particle-filtering framework. We developed a motion-based Particle filter that is robust to sharp movements of the tracked object while propagating few particles; thus capturing robustness and efficiency. For post-tracking contour refinement, we use a 1-D causal active contour representation based on dynamic programming to find the best local contour delineating a non-rigid object. Since the traditional active contour model suffers from its dependency on the model parameters and initial condition as a consequence of local minima in the cost function, we improved the convergence of the active contour by performing the optimization over multiple randomly sampled initial conditions. Our experiments, applied to object tracking in challenging real-world videos, demonstrate the dramatic improvement of the proposed motion-based particle filter and randomly perturbed active contour system.

Related publications:

  1.    N. Bouaynaya and D. Schonfeld, “On the Optimality of Motion-Based Particle Filtering”, IEEE Transactions on Circuits and Systems for Video Technology, accepted.    

  2.   N. Bouaynaya and D. Schonfeld, “Motion-based particle filtering for head tracking applications,” (Invited Paper). Electronic Imaging Newsletter, vol. 15, no. 2, p. 8, 2005.  

  3.   N. Bouaynaya and D. Schonfeld, "Complete system for head tracking using motion-based particle filter and randomly perturbed active contour", in Proceedings of SPIE, Image and Video Communications and Processing (IVCP'05), vol.5685, March 2005, pp. 864-873. (Finalist for Best Student Paper Award).  

  4.    N. Bouaynaya, W. Qu and D. Schonfeld, "An Online Motion-Based Particle Filter for Head Tracking Applications", in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'05), vol. 2, March 18-23, 2005, pp. 225 - 228.  

  5.   W. Qu, N. Bouaynaya and D. Schonfeld, "Automatic Multi-Head Detection and Tracking System using A Novel Detection-Based Particle Filter and Data Fusion", in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'05), vol. 2, March 18-23, 2005, pp. 661 - 664.  

Some Experiments:


     Courtesy of Stan Birchfield        My labmate Vivek Nigam         My labmate Wei Qu