Feature-Scoring-Based multi-cue infrared object tracking

Authors:
Jiangtao Wang;Debao Chen;Suwen Li;Yijun Yang
Affiliations:
Huaibei Normal University, Huaibei, China;Huaibei Normal University, Huaibei, China;Huaibei Normal University, Huaibei, China;Huaibei Normal University, Huaibei, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

Citing 4
Cited 0

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
Real-time hand-tracking with a color glove

ACM SIGGRAPH 2009 papers

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an effective tracker for infrared videos based on the multi-cue fusion. Under the particle filter tracking construction, a novel feature scoring scheme is introduced to evaluate different cue tracking ability, then the multi-cue fusion is executed in a weighted sum manner. In our tracking system, the score of each feature can be adaptively updated according to the current environment. Experimental results with various Infrared Video Database and different trackers are reported to demonstrate the accuracy and robustness of our algorithm.