Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking

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.