Abstract
Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter.
Highlights
Applications in the computer vision field such as surveillance, video indexing, traffic monitoring, and auto-driving have come into our life
We propose a novel mean-shift tracker based on the neutrosophic similarity score [21,30] under the single valued neutrosophic set (SVNS) environment
Success plots of temporal robustness evaluation (TRE) and one-pass evaluation (OPE) for the whole testing sequences are shown in Figures 6a and 7a, and the success plots for those sequences including background clutter challenge are shown in Figures 6b and 7b
Summary
Applications in the computer vision field such as surveillance, video indexing, traffic monitoring, and auto-driving have come into our life. By utilizing the color histogram feature and the efficient seeking method, such a mean-shift tracker demonstrates high efficiency and good performance, even when confronting motion blur and deformation problems. Since estimating an adequate scale is essential for robust tracking, a more robust method for estimating the scale of the searching bounding box was proposed through the forward–backward consistency check This mean-shift based tracker [13] outperforms several state-of-the-art algorithms. The remainder of this paper is organized as follows: in Section 2, the traditional mean-shift procedure for visual object tracking and the definition of the neutrosophic similarity score are first given. The details of the method for calculating the neutrosophic weight histogram are presented, and the main steps of the proposed mean-shift tracker are illustrated in the following subsection.
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