A Fast Hyperspectral Tracking Method via Channel Selection

Abstract

With the rapid development of hyperspectral imaging technology, object tracking in hyperspectral video has become a research hotspot. Real-time object tracking for hyperspectral video is a great challenge. We propose a fast hyperspectral object tracking method via a channel selection strategy to improve the tracking speed significantly. First, we design a strategy of channel selection to select few candidate channels from many hyperspectral video channels, and then send the candidates to the subsequent background-aware correlation filter (BACF) tracking framework. In addition, we consider the importance of local and global spectral information in feature extraction, and further improve the BACF tracker to ensure high tracking accuracy. In the experiments carried out in this study, the proposed method was verified and the best performance was achieved on the publicly available hyperspectral dataset of the WHISPERS Hyperspectral Objecting Tracking Challenge. Our method was superior to state-of-the-art RGB-based and hyperspectral trackers, in terms of both the area under the curve (AUC) and DP@20pixels. The tracking speed of our method reached 21.9 FPS, which is much faster than that of the current most advanced hyperspectral trackers.