In this paper, we propose a On-line kernel-PLS approach to improving both the robustness and accuracy of object tracking which is appropriate for real-time video surveillance. Typical tracking with color histogram matching provides robustness but has insufficient accuracy, because it does not involve spatial information. On the other hand, tracking with pixel-wise matching achieves accurate per...

Mobile object tracking has an important role in the computer vision applications. In this paper, we use a tracked target-based taxonomy to present the object tracking algorithms. The tracked targets are divided into three categories: points of interest, appearance and silhouette of mobile objects. Advantages and limitations of the tracking approaches are also analyzed to find the future directi...

In this paper, we propose an object tracking framework based on a spatial pyramid heat kernel structural information representation. In the tracking framework, we take advantage of heat kernel structural information (HKSI) matrices to represent object appearance, because HKSI matrices perform well in characterizing the edge flow (or structural) information on the object appearance graph. To fur...

This paper describes new computer vision algorithms that have been developed to track moving objects as part of a long-term study into the design of (semi-)autonomous vehicles. We present the results of a study to exploit variable kernels for tracking in video sequences. The basis of our work is the mean shift object-tracking algorithm; for a moving target, it is usual to define a rectangular t...

In recent years, due to the video surveillance applications more and more widely, people are not satisfied with the goal of monitoring, and the video monitoring technology of intelligent video moving object detection and tracking technology has received extensive attention. The research work in this paper is in the field, the moving target detection spatiotemporal correlation and difference con...

In this paper a novel tracking feature selection method is presented. Assuming the features that best discriminate between object and background are also best for tracking the object. A two-class variance ratio is employed to measure the discriminability. Genetic algorithm is used to optimize the different features combination to generate the best tracking feature. To demonstrate our proposed m...

This paper proposes a general Kernel-Bayesian framework for object tracking. In this framework, the kernel based method—mean shift algorithm is embedded into the Bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional Bayesian t...

multiple people detection and tracking is a challenging task in real-world crowded scenes. in this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. we have detected objects with deformable part models and a visual background extractor. in the tracking phase we have used a combination of support vector machine (svm) person-specific classifie...