On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making:A Converse Lyapunov Approach

Motivated by the converse Lyapunov technique for investigating converse results of semistable switched systems in control theory,this paper utilizes a constructive induction method to identify a cost function for performance gauge of an average,multi-cue multi-choice(MCMC),cognitive decision making model over a switching time interval.It shows that such a constructive cost function can be evaluated through an abstract energy called Lyapunov function at initial conditions.Hence,the performance gauge problem for the average MCMC model becomes the issue of finding such a Lyapunov function,leading to a possible way for designing corresponding computational algorithms via iterative methods such as adaptive dynamic programming.In order to reach this goal,a series of technical results are presented for the construction of such a Lyapunov function and its mathematical properties are discussed in details.Finally,a major result of guaranteeing the existence of such a Lyapunov function is rigorously proved.

Robust visual tracking algorithm based on Monte Carlo approach with integrated attributes

To improve the reliability and accuracy of visual tracker,a robust visual tracking algorithm based on multi-cues fusion under Bayesian framework is proposed.The weighed color and texture cues of the object are applied to describe the moving object.An adjustable observation model is incorporated into particle filtering,which utilizes the properties of particle filter for coping with non-linear,non-Gaussian assumption and the ability to predict the position of the moving object in a cluttered environment and two complementary attributes are employed to estimate the matching similarity dynamically in term of the likelihood ratio factors;furthermore tunes the weight values according to the confidence map of the color and texture feature on-line adaptively to reconfigure the optimal observation likelihood model,which ensured attaining the maximum likelihood ratio in the tracking scenario even if in the situations where the object is occluded or illumination,pose and scale are time-variant.The experimental result shows that the algorithm can track a moving object accurately while the reliability of tracking in a challenging case is validated in the experimentation.

Relative discriminant coefficient based multi-cue fusion for robust object tracking

In visual tracking,integrating multiple cues will increase the reliability and robustness of the tracking system in situations where no single cue is reliable.In this paper,a novel multi-cue based tracking method is presented under the particle filter framework.Considering both practical distance and Bhattacharyya distance between particles and the target,a parameter called relative discriminant coefficient(RDC)is designed to measure the tracking ability for different features.Multi-cue fusion is carried out in a reweighing manner based on this parameter.Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes,cluttered background,illumination changes and occlusions.