The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target’s saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination. 1. Introduction Target tracking has attracted a lot of attention in computer vision due to its fundamental importance for many vision applications such as visual surveillance, traffic safety monitoring, and abnormal activity detection. And many successful techniques of target tracking have been proposed in the last several decades [1]. The applicability of the techniques in general scenarios, however, is still very limited due to practical difficulties: appearance variations (e.g., illumination, viewpoint, and background changes), occlusions, complex backgrounds, and so forth. These difficulties are inevitable in practical applications and thus noticeably aggravate this problem [2]. To overcome these problems, many researchers have proposed numerous target tracking methods. In many traditional approaches, various kinds of low-level observation models have been used for object tracking, such as feature points [3], lines or templates [4], moving areas [5], and color appearance models [6]. The common framework for target tracking algorithm mainly includes mean-shift method using Kalman filter and particle filtering algorithm. The particle filter is a filtering algorithm based on Bayesian inference, through nonparametric sequential Monte Carlo methods. The particle filter is not linear and Gaussian distribution systems to meet the restrictions are widely used in navigation, machine vision, target tracking and so on. We can use the variety of particle filtering features, colors, and edge contour. They are more
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