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Target tracking with online feature selection in FLIR imagery
| Content Provider | CiteSeerX |
|---|---|
| Author | Venkataraman, Vijay Fan, Xin Fan, Guoliang |
| Abstract | We present a particle lter-based target tracking algo-rithm for FLIR imagery. A dual foreground and background model is proposed for target representation which supports robust and accurate target tracking and size estimation. A novel online feature selection technique is introduced that is able to adaptively select the optimal feature to maximize the tracking condence. Moreover, a coupled particle lter-ing approach is developed for joint target tracking and fea-ture selection in an unied Bayesian estimation framework. The experimental results show that the proposed algorithm can accurately track poorly-visible targets in FLIR imagery even with strong ego-motion. The tracking performance is improved when compared to the tracker with a foreground-based target model and without online feature selection. 1. |
| File Format | |
| Journal | Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Access Restriction | Open |
| Subject Keyword | Coupled Particle Lter-ing Approach Background Model Fea-ture Selection Flir Imagery Dual Foreground Tracking Performance Size Estimation Poorly-visible Target Online Feature Selection Novel Online Feature Selection Technique Particle Lter-based Target Optimal Feature Strong Ego-motion Accurate Target Tracking Unied Bayesian Estimation Framework Tracking Condence Target Representation Joint Target Foreground-based Target Model |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings |
