Abstract
A detection algorithm is often used to remedy tracking failures in a typical single-target visual tracking algorithm. In practice, when a target is occluded for a long time, neither the tracking module nor the detection module can accurately predict the position of the target. To accurately locate the target, we first introduce the l 1 l 2 loss function to reduce the sensitivity of correlation filter-based method to local occlusion. To solve the instability of algorithms based on the single feature in complex scene, we use the histogram of oriented gradient (HOG) features and color names (CN) features to train a filter respectively, and the fusion weights are calculated according to the difference between the response value of each filter and the expected response value. At the same time, we adaptively update the model online by calculating the sensitivity of different filters. We follow the re-detection idea in long-term tracking, the peak to sidelobe ratio (PSR) is used to judge the serious occlusion, and we use support vector machine (SVM) for re-detection after severe occlusion or target out-of-view. In this paper, 34 sets of sequences are selected to evaluate the proposed algorithm. The sufficient experimental results demonstrate that our algorithm has strong anti-occlusion ability and robustness performance. We compare our proposal with several state-of-the-art algorithms under all the sequences of OTB100, and our algorithm yields highly competitive performance for tracking.
Highlights
Visual tracking is one of the most basic problems in computer vision
The contributions of this paper can be summarized in the following three aspects: 1. Unlike other correlation filter (CF)-based methods, we introduce 1 2 loss function to reduce the sensitivity to local occlusion, which is a great challenge for object tracking
To solve the instability of the algorithms based on the single feature in complex scenes, we extract the histogram of oriented gradient (HOG) and color names (CN) features to train the model and track the target
Summary
Visual tracking is one of the most basic problems in computer vision. In brief, given the target information of the first frame, the visual tracking task evaluates the target location in the subsequent frames. Some long-term tracking algorithms introduced re-detection strategies to solve the problem of the target out-of-view [18]–[20]. These methods use a global search or re-detection strategy to retrieve the target. 2. A new visual tracking framework combined with re-detection is proposed to perform robust tracking, including a novel adaptive online model update strategy based on feature fusion. A new visual tracking framework combined with re-detection is proposed to perform robust tracking, including a novel adaptive online model update strategy based on feature fusion It iteratively conducts adaptive learning on a variety of features and can adapt to a variety of challenging scenarios. The sufficient results show that the proposed algorithm has a satisfactory antiocclusion ability and robustness
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