Name
Abstract | ||
|---|---|---|
This paper presents a method of extracting discriminative stable regions (DSRs) from image, and applies them for object tracking. These DSRs obtained by using the criterion of maximal entropy and spatial discrimination present high appearance stability and strong spatial discriminative power, which enables them to tolerate more appearance variations and to effectively resist spatial distracters. Meanwhile, the adaptive fusion tracking incorporated k-means clustering can handle severe occlusion as well as disturbance of motion noise during target localization. In addition, an effective local update scheme is designed to adapt to the object change for ensuring the tracking robustness. Experiments are carried out on several challenging sequences and results show that our method performs well in terms of object tracking, even in the presence of occlusion, deformation, illumination change, moving camera and spatial distracter. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.12.020 | Neurocomputing |
Keywords | Field | DocType |
robust visual tracking,spatial distracters,discriminative stable region,tracking robustness,object tracking,illumination change,appearance variation,spatial distracter,k-means clustering,spatial discrimination,object change,strong spatial discriminative power,k means clustering,visual tracking | Computer vision,k-means clustering,Pattern recognition,Robustness (computer science),Eye tracking,Video tracking,Artificial intelligence,Cluster analysis,Discriminative model,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
111, | 0925-2312 | 4 |
PageRank | References | Authors |
0.37 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Canlong Zhang | 1 | 15 | 1.54 |
| Zhongliang Jing | 2 | 351 | 39.38 |
| Han Pan | 3 | 10 | 2.15 |
| Bo Jin | 4 | 42 | 12.84 |
| Zhixin Li | 5 | 111 | 24.43 |