Affiliations: [a] Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China
| [b] Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
Correspondence:
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Corresponding author. Keli Hu, Department of Computer Science and Engineering, Shaoxing University, Shaoxing 312000, Zhejiang, China. Tel.: +86 575 88341512; E-mail: [email protected].
Abstract: Although appearance based trackers have been greatly improved in the last decade, they are still struggling with some challenges like occlusion, blur, fast motion, deformation, etc. As known, occlusion is still one of the soundness challenges for visual tracking. Other challenges are also not fully resolved for the existed trackers. In this work, we focus on tackling the latter problem in both color and depth domains. Neutrosophic set (NS) is as 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 build a robust tracker. First, the color and depth histogram are employed as the appearance features, and both features are represented in the SVNS domain via three membership functions T, I, and F. Second, the single valued neutrosophic cross-entropy measure is utilized for fusing the color and depth information. Finally, a novel SVNS based MeanShift tracker is proposed. Applied to the video sequences without serious occlusion in the Princeton RGBD Tracking dataset, the performance of our method was compared with those by the state-of-the-art trackers. The results revealed that our method outperforms these trackers when dealing with challenging factors like blur, fast motion, deformation, illumination variation, and camera jitter.
