Name
Title | ||
|---|---|---|
A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization. |
Abstract | ||
|---|---|---|
•A new hybrid gravitational search algorithm (HGSA) is proposed for object tracking in video stream.•HSGA shows high accuracy rate using videos from the online tracking benchmark.•Accuracy rate is increased 50.6% with respect to the best existing Swarm Intelligence based tracker. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2018.02.037 | Applied Soft Computing |
Keywords | Field | DocType |
Gravitational search algorithm,Particle swarm optimization,Deep convolutional neural network,Object tracking | Convergence (routing),Particle swarm optimization,BitTorrent tracker,Histogram,Weight function,Pattern recognition,Swarm intelligence,Robustness (computer science),Video tracking,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
66 | 1568-4946 | 5 |
PageRank | References | Authors |
0.38 | 31 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyuchang Kang | 1 | 127 | 14.39 |
| Changseok Bae | 2 | 161 | 23.90 |
| Henry Wing Fung Yeung | 3 | 24 | 3.01 |
| Yuk Ying Chung | 4 | 211 | 25.47 |