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 ...

Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensiona...

A distributed target tracking algorithm based on particle filter is presented to improve the tracking accuracy. By integrating the maximum likelihood estimation algorithm into particle filter model, an optimum weight selection algorithm can be used to reduce overall energy consumption of some sample calculations activities in wireless sensor network. Evaluation results prove that the improved s...

We propose a new method for tracking rigid objects in image sequences using template matching. A Kalman filter is used to make the template adapt to changes in object orientation or illumination. This approach is novel since the Kalman filter has been used in tracking mainly for smoothing the object trajectory. The performance of the Kalman filter is further improved by employing a robust and a...

Motivation. Correlation filter based tracking has attracted many researchers’ attention in recent years for high efficiency and robustness. Most existing works [1, 2, 4] focus on exploiting different characteristics with correlation filters for visual tracking, e.g., circulant structure, kernel trick, effective feature representation and context information. Despite its good performance, most o...