Name
Abstract | ||
|---|---|---|
Visual tracking is of great significance in computer vision. In this paper, we propose a long-term tracking method to deal with appearance variation caused by occlusion, fast motion, out-of-view, etc. Firstly, we extract CNNs features from both low and high depth of a pre-trained VGGNet and estimate target translation via correlation filters separately trained on features from three different layers. Then a HOG based scale correlation filter is applied to search target pyramids cropped around target position for optimal scale. In case of tracking failure, we train a SVM to re-detect the target. In addition, scale model is only updated when scale response is higher than a pre-defined threshold. Experimental results on OTB2013 show that our algorithm is of effectiveness and robustness in case of heavy appearance changes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-7305-2_57 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Correlation filters,CNNs features,Long-term tracking,Scale estimation | Conference | 773 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huizhi Chen | 1 | 0 | 0.68 |
| Baojie Fan | 2 | 41 | 10.48 |