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Making visual features/trackers cooperate together allows to benefit from the complementary of different features and explores the merits of each individual tracker, which increases the robustness of video tracking. In this paper, two trackers are made in cooperation. The main tracker is based on particle filter and uses a visual model combining color and gradient orientations as the target representation,...
A new visual object tracking algorithm is proposed by using joint feature points correspondences and color similarity of the moving object to solve the background disturbance. This tracking algorithm is based on particle filtering in which a new method of computing each sample weight is proposed. Each sample weight can be obtained through measuring the similarities of color histogram and feature points...
Object tracking based on color feature often fails in a complex background. To deal with this problem, a particle filtering object tracking approach is proposed in this paper based on local binary pattern and color feature. Color histogram is the global description of targets in color image, while local binary pattern texture contains information of neighbor region texture in gray image. These two...
Practical tracking system must be able to adjust the tracking windows adaptively according to the size-changes of the tracked objects; otherwise it can not track the objects with obvious size-changes accurately. Based on the visual theory, and combined with the primal sketch of the objects extracted by the Otsu method as well as the changes of the elements-number as the measure information, this paper...
Particle filtering is a popular algorithm for vision-based target tracking. Despite its effectiveness in many fields of tracking, however, the computation requirement of particle filters is high. In this paper we propose an algorithm and architecture for vision-based particle filters. The proposed algorithm can estimate objects' positions, sizes, and angles by using color histogram as the feature...
In this article, we propose a new observation model combination approach under particle filtering scheme, which allows robust and accurate visual tracking under typical circumstances of real-time visual tracking. This scheme stochastically selects single observation model to evaluate the likelihood of some particle. Since only one single observation likelihood is evaluated for any one particle, the...
In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified...
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