KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Twofold Correlation Filtering for Tracking Integration | |
Wang, Wei1,2 ; Li, Weiguang1,2; Chen, Zhaoming1 ; Shi, Mingquan1
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| 2018-10-01 | |
| 摘要 | In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising. |
| 关键词 | object tracking correlation filter end-to-end represent learning complementary features trackers integration |
| DOI | 10.1587/transinf.2018EDL8100 |
| 发表期刊 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
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| ISSN | 1745-1361 |
| 卷号 | E101D期号:10页码:2547-2550 |
| WOS记录号 | WOS:000445874200017 |
| 语种 | 英语 |
