Abstract
In this paper, we propose a robust visual tracking method based on mutual kernelized correlation filters with elastic net constraint. First, two correlation filters are trained in a general framework jointly in a closed form, which are interrelated and interacted on each other. Second, elastic net constraint is imposed on each discriminative filter, which is able to filter some interfering features. Third, scale estimation and target re-detection scheme are adopted in our framework, which can deal with scale variation and tracking failure effectively. Extensive experiments on some challenging tracking benchmarks demonstrate that our proposed method is able to obtain a competitive tracking performance against other state-of-the-art algorithms.
Highlights
Visual tracking is a fundamental task in computer vision with numerous applications, such as unmanned control systems, surveillance, assistant driving, and so on
Inspired by the above discussions, we develop a robust visual tracking method via mutual kernelized correlation filters using features from convolutional neural networks (MKCN_CNN), where each tracker works on its own and tries to correct the other one
3 Methods Though the kernelized correlation filters (KCF) method has obtained promising tracking performance, only one discriminative classifier is used in this model, which makes the KCF method not able to deal with complex sciences
Summary
Visual tracking is a fundamental task in computer vision with numerous applications, such as unmanned control systems, surveillance, assistant driving, and so on. Generative methods attempt to build a model to represent tracked target and find the region with the minimum reconstruction error from a great deal of candidates. In order to solve this problem, Bao et al [15] proposed a fast l1 tracking method by using accelerated proximal gradient approach. Xiao et al [16] presented a fast object tracking method by solving l2 regularized least square problem. Wang et al [17] developed a novel and fast visual tracking method via probability continuous outlier model. Different from the general method, discriminative algorithms regard visual tracking as a binary classification problem which distinguishes the correct tracked object from the background. Babenko et al [18] trained an online discriminative classifier to separate the tracked object from the background by online multiple instance learning. Zhang et al [19] formulated visual tracking as a binary classification via a naive
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