Name
Abstract | ||
|---|---|---|
Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions. © 2010 IEEE. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICCA.2010.5524128 | ICCA |
Keywords | Field | DocType |
evaluation function,mobile robots,automatic control,particle filters,computer vision,particle filter,computational modeling,likelihood function,tracking,mobile robot,robustness,probability,visual tracking,color | Robotic systems,Computer vision,Open problem,Computer science,Particle filter,Evaluation function,Tracking system,Eye tracking,Artificial intelligence,Probabilistic logic,Mobile robot | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |