hand gesture recognition is very important to communicate in sign language. in this paper, an effective object tracking and hand gesture recognition method is proposed. this method is combination of two well-known approaches, the mean shift and the motion detection algorithm. the mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. several...

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian....

This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated bo...

This document describes the progress on the MUCATAR (MUltiple CAmera Tracking and Activity Recognition) IM2 White Paper Project during its second year. Building on the first year achievments on single-object tracking, the research during the second year moved into two main directions: 1) the investigation of new sampling strategies to improve tracking with particle filters, both for single and ...

Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this study, firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process ...