Abstract
Discriminative correlation filter- (DCF-) based trackers are computationally efficient and achieve excellent tracking in challenging applications. However, most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image (visible spectrum). Fusion of visible and infrared imaging sensors, one of the typical multisensor cooperation, provides complementarily useful features and consistently helps recognize the target from the background efficiently in visual tracking. Therefore, this paper proposes a discriminative fusion correlation learning model to improve DCF-based tracking performance by efficiently combining multiple features from visible and infrared images. Fusion learning filters are extracted via late fusion with early estimation, in which the performances of the filters are weighted to improve the flexibility of fusion. Moreover, the proposed discriminative filter selection model considers the surrounding background information in order to increase the discriminability of the template filters so as to improve model learning. Extensive experiments showed that the proposed method achieves superior performances in challenging visible and infrared tracking tasks.
Highlights
Visual tracking has received widespread attention for its extensive applications in video surveillance, autonomous driving and human-machine interaction, military attack, robot vision, etc. [1, 2]
Most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image
Most of the Discriminative correlation filter- (DCF-)based trackers suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image
Summary
Visual tracking has received widespread attention for its extensive applications in video surveillance, autonomous driving and human-machine interaction, military attack, robot vision, etc. [1, 2]. Generative tracking algorithms build a target model and search for the candidate image patch with maximal similarity. Chi et al [4] trained a dual network with random patches measuring the similarities between the network activation and target appearance to leverage the robustness of visual tracking. The goal of discriminative algorithms is to learn a classifier to discriminate between its appearance and that of the environment given an initial image patch containing the target. Yang et al [5] proposed a temporal restricted reverse-low-rank learning algorithm for visual tracking to jointly represent target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level. A new peak strength metric [6] is proposed to measure the discriminative capability of the learned correlation filter that can effectively strengthen the peak of the correlation response, leading to more discriminative performance than previous methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
