Name
Abstract | ||
|---|---|---|
Numerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TPAMI.2015.2417577 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
object tracking,benchmark database,performance evaluation | Torso,Computer vision,Pattern recognition,Computer science,Eye tracking,Video tracking,Artificial intelligence | Journal |
Volume | Issue | ISSN |
PP | 99 | 0162-8828 |
Citations | PageRank | References |
44 | 1.13 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Annan | 1 | 44 | 1.13 |
| Min Lin | 2 | 549 | 24.01 |
| Yi Wu | 3 | 2347 | 70.10 |
| Yang Ming-Hsuan | 4 | 15303 | 620.69 |
| Shuicheng Yan | 5 | 9701 | 359.54 |