Dense structural learning for infrared object tracking at 200+ Frames per Second

摘要

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. (C) 2017 Elsevier B.V. All rights reserved.