A novel object tracking algorithm by fusing color and depth information based on single valued neutrosophic cross-entropy

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.

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

“…Until now, NS has been successfully applied in many areas [22]. For the computer vision research fields, the NS theory is widely utilized in image segmentation [17][18][19][20][21], skeleton extraction [23] and object tracking [24], etc. Before calculating the segmentation result for an image, a specific neutrosophic image was usually computed via several criteria in NS domain [17][18][19][20][21].…”

“…Before calculating the segmentation result for an image, a specific neutrosophic image was usually computed via several criteria in NS domain [17][18][19][20][21]. For object tracking, in order to improve the traditional color based CAMShift tracker, the single valued neutrosophic cross-entropy was employed for fusing color and depth information [24]. In addition, the NS theory is also utilized for improving the clustering algorithms, such as c-means [25].…”

Element-Weighted Neutrosophic Correlation Coefficient and Its Application in Improving CAMShift Tracker in RGBD Video

“…The correlation coefficient between SVNSs [17] was applied for calculating a neutrosophic score-based image [9], and a robust threshold was estimated by employing the OTSU's method [9]. In [11], two criteria were proposed in both color and depth domain. The information fusion problem was converted into a multicriteria decision-making issue, and the single-valued neutrosophic cross-entropy was employed to tackle this problem [11].…”

“…In [11], two criteria were proposed in both color and depth domain. The information fusion problem was converted into a multicriteria decision-making issue, and the single-valued neutrosophic cross-entropy was employed to tackle this problem [11]. For the neutrosophic theory-based MeanShift tracker [12], by taking the consideration of the background information and appearance changes between frames, two kinds of criteria were considered, the object feature similarity and the background feature similarity.…”

“…The other way is to train robust models, such as the multiple instance learning [31,32] and compressive sensing [33]-based tracking. Besides the kinds of trackers mentioned above, the local-global tracker (LGT) [34], incremental visual tracker (IVT) [35] and tracking-learning-detection (TLD) [36] also perform well.Recently, due to the fact that the depth information can provide another dimension for tackling the object tracking problem, some RGBD-based trackers [11,[37][38][39] have been proposed. Most algorithms are oriented to specific targets [37][38][39].…”