Abstract
Mean-shift tracking has gained more interests, nowadays, aided by its feasibility of real-time and reliable tracker implementation. In order to reduce background clutter interference to mean-shift object tracking, this paper proposes a novel indicator function generation method. The proposed method takes advantage of two ‘a priori’ knowledge elements, which are inherent to a kernel support for initializing a target model. Based on the assured background labels, a gradient-based label propagation is performed, resulting in a number of objects differentiated from the background. Then the proposed region growing scheme picks up one largest target object near the center of the kernel support. The grown object region constitutes the proposed indicator function and this allows an exact target model construction for robust mean-shift tracking. Simulation results demonstrate the proposed exact target model could significantly enhance the robustness as well as the accuracy of mean-shift object tracking.
Highlights
Object tracking, as one of the most fundamental tasks in computer vision, has many applications such as video indexing, automated surveillance, and human computer interaction, etc
In order to reduce the interference of background clutter, this paper proposes a novel indicator function generation method and demonstrates an exact target model construction that could significantly enhance the robustness as well as the accuracy of kernel-based tracking results
For more accurate and robust performance of such mean-shift tracking, this paper introduced a target model construction based on an indicator function and provided an algorithm to create the indicator function
Summary
As one of the most fundamental tasks in computer vision, has many applications such as video indexing, automated surveillance, and human computer interaction, etc. Proposed a color-histogram-based object tracker using the Bhattacharyya similarity measure and Epanechnikov ellipsoidal kernel They developed a mean-shift target localization scheme, which quickly and reliably optimizes the similarity between the kernel-weighted histogram of a target and that of the candidate region. In order to reduce the interference of background clutter, this paper proposes a novel indicator function generation method and demonstrates an exact target model construction that could significantly enhance the robustness as well as the accuracy of kernel-based tracking results. The rest of this paper is organized as follows: Section 2 explains the conventional method of mean-shift object tracking with various previous background weighting algorithms to explain how an indicator function is used for constructing the target model of the proposed tracking scheme.
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