Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking

• Published in: Neurocomputing (Amsterdam) 2020-07, Vol.398, p.235-246
• Main Authors: Dai, Manna, Xiao, Gao, Cheng, Shuying, Wang, Dadong, He, Xiangjian
• Format: Article
• Language: English
• Subjects: Convolutional Neural Network (CNN); Gaussian Particle Filter (GPF); Lukas-Kanade (LK); Reliability estimation; Structural correlation filter
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2020.02.095

•A homogeneous ensemble strategy is proposed to provide preliminary locating.•A motion detection method based on Lukas-Kanade is utilized for tackling boundary effects.•A facile weight strategy is designed to measure reliability degrees of weak classifiers.•The GPF with CNN features is adopted to execute re-detection and scale estimation of a target. Visual tracking is a key problem for many computer vision applications such as human-computer interaction, intelligent medical diagnosis, navigation and traffic control management. Most of the existing tracking methods are mainly based on correlation filters. However, boundary effect, scale estimation and template updating have not been fully resolved. Herein, this paper presents a new hierarchical tracking method combining structural correlation filters with a Gaussian Particle Filter (GPF), named KCF-GPF. Weak KCF classifiers are constructed via a Lukas-Kanade (LK) method and the preliminary target location is presented as a weighted sum of these classifiers. Specially, a facile weight strategy is implemented to estimate the reliability of each weak classifier. On the basis of the preliminary target location, the GPF using features from a Convolutional Neural Network (CNN) is employed to predict the location and scale of a target. Extensive experiments with the OTB-2013 and the OTB-2015 databases demonstrate that the proposed algorithm performs favourably against state-of-the-art trackers.