Name
Abstract | ||
|---|---|---|
We propose a novel tracking method using color features and texture features to obtain accurate target appearance model. Each feature dictionary is independent and learned by labeled consistent K-SVD. In the subsequent frames, we exploit the maximum similarity of sparse features by the minimal reconstruction error criterion to locate the best tracking result. When a significant change occurs, we propose an adaptive dictionary learning which update the background template incrementally using the positive and negative samples of the current target. We compared our method with the existing techniques in OTB100 and VOT2017 dataset, and the experimental results show that our proposed method achieved substantially better performance. |
Year | Venue | Field |
|---|---|---|
2018 | PRCV | Dictionary learning,Pattern recognition,Computer science,Sparse approximation,Reconstruction error,Exploit,Active appearance model,Eye tracking,Artificial intelligence,Discriminative model |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Penggen Zheng | 1 | 0 | 0.34 |
| Jin Zhan | 2 | 39 | 3.57 |
| Huimin Zhao | 3 | 206 | 23.43 |
| Jujian Lv | 4 | 0 | 1.35 |