Name
Abstract | ||
|---|---|---|
This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1587/transinf.E95.D.646 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
object tracking, hybrid feature, particle filter grayscale arranging pairs (GAP), color histograms | Computer vision,Pattern recognition,Color histogram,Feature (computer vision),Computer science,Particle filter,Video tracking,Artificial intelligence,Discriminative model,Grayscale | Journal |
Volume | Issue | ISSN |
E95D | 2 | 1745-1361 |
Citations | PageRank | References |
2 | 0.38 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyue Zhao | 1 | 77 | 11.37 |
| Yutaka Satoh | 2 | 81 | 19.19 |
| Hidenori Takauji | 3 | 21 | 1.96 |
| Shun'ichi Kaneko | 4 | 230 | 35.34 |