Name
Title | ||
|---|---|---|
Context-Aware Three-Dimensional Mean-Shift With Occlusion Handling for Robust Object Tracking in RGB-D Videos |
Abstract | ||
|---|---|---|
Depth cameras have recently become popular and many vision problems can be better solved with depth information. But, how to integrate depth information into a visual tracker to overcome the challenges such as occlusion and background distraction is still underinvestigated in current literature on visual tracking. In this paper, we investigate a 3-D extension of a classical mean-shift tracker whose greedy gradient ascend strategy is generally considered as unreliable in conventional 2-D tracking. However, through careful study of the physical property of 3-D point clouds, we reveal that objects which may appear to be adjacent on a 2-D image will form distinctive modes in the 3-D probability distribution approximated by kernel density estimation, and finding the nearest mode using 3-D mean-shift can always work in tracking. Based on the understanding of 3-D mean-shift, we propose two important mechanisms to further boost the tracker's robustness: one is to enable the tracker to be aware of potential distractions and make corresponding adjustments to the appearance model; and the other is to enable the tracker to detect and recover from tracking failures caused by total occlusion. The proposed method is both effective and computationally efficient. On a conventional personal computer, it runs at more than 60 FPS without graphical processing unit acceleration. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMM.2018.2863604 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Three-dimensional displays,Target tracking,Cameras,Two dimensional displays,Image color analysis,Histograms | Histogram,Computer vision,Pattern recognition,Computer science,Personal computer,Active appearance model,Robustness (computer science),Video tracking,Eye tracking,Artificial intelligence,Mean-shift,Kernel density estimation | Journal |
Volume | Issue | ISSN |
21 | 3 | 1520-9210 |
Citations | PageRank | References |
4 | 0.55 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Liu | 1 | 8 | 3.34 |
| Xiao-Yuan Jing | 2 | 769 | 55.18 |
| Jianhui Nie | 3 | 4 | 0.89 |
| Hao Gao | 4 | 8 | 6.38 |
| Jun Liu | 5 | 671 | 30.44 |
| Guo-Ping Jiang | 6 | 25 | 6.26 |