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The deep learning based trackers can always achieve high tracking precision and strong adaptability in different scenarios. However, due to the fact that the number of the parameter is large and the fine-tuning is challenging, the time complexity is high. In order to improve the efficiency, we proposed a tracker based on fast deep learning through constructing a new network with less redundancy. Based...
Most traditional visual tracking algorithms ignore the importance of tracking failure detection which is helpful for occlusion handling. To address this problem, a robust visual tracking algorithm combining the tracking model and the failure detection strategy is proposed for the needs of practical applications. In order to tracking the position and scale of the target with high speed and accuracy,...
This paper proposes a superpixel tracking method via a graph-based hybrid discriminative-generative appearance model. By utilizing a superpixel-based graph structure as the visual representation, spatial information between superpixels is considered. For constructing the discriminative appearance model, we propose a graph-based semi-supervised support vector machine (SVM) approach by taking superpixels...
Orange technologies focus on individual behavior analysis, and the core of which is object tracking, especially arbitrary object tracking. One of the popular solution for arbitrary object tracking is tracking by detection. These approaches regard the tracking problem as a detection task, and use the online learning methods to adapt the classifier to various object appearance changes. However, due...
Many object tracking methods based on Adaptive Appearance Models (online learning methods) have been developed in recent years. One problem that can be found with these methods is how to learn variations in object appearance without errors in the image sequence. This paper introduces a novel method, in which a solution to remove learning errors by using an offline learning is proposed; in addition,...
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