Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6940812 | Pattern Recognition Letters | 2017 | 9 Pages |
Abstract
Infrared object tracking is a key technology in many surveillance applications. General visual tracking algorithms designed for color images can not handle infrared targets very well due to their relatively low resolutions and blurred edges. This paper presents a new tracking by detection method based on online structural learning. We show how to train the classifier efficiently with dense samples through Fourier techniques and careful implementation. Furthermore, we introduce an effective feature representation for infrared objects. Finally, we demonstrate the performance of the proposed tracker on public infrared sequences with top accuracy and robustness. Meanwhile, our single thread C++ implementation of the algorithm achieves an average tracking speed of 215 FPS on a modern cpu.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xianguo Yu, Qifeng Yu, Yang Shang, Hongliang Zhang,