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Deep neural network-based methods have recently achieved excellent performance in visual tracking task. As very few training samples are available in visual tracking task, those approaches rely heavily on extremely large auxiliary dataset such as ImageNet to pretrain the model. In order to address the discrepancy between the source domain (the auxiliary data) and the target domain (the object being...
Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much...
Evaluating the performance of multi-target tracking with respect to tracks rather than unlabeled estimated points is important and challenging. Existing approaches assume exact knowledge of the ground truth. However, this is far from the reality. This paper proposes a method to deal with the case of unknown ground truth by measuring the difference between mock tracks and the assumed targets in the...
Visual Tracking is a fundamental task in computer vision which has been extensively researched. Though much progress exists in literature, it is still very challenging due to factors such as partial occlusions, pose variations, viewpoint variations and so on. In this paper, we address the visual tracking problem in a discriminant manner where a simple convolutional neural network (CNN) is employed...
Joint tracking and classification (JTC) is rapidly gaining momentum recently. Algorithms have been proposed for this problem. However, performance of tracking and classification has been evaluated separately without considering their interdependence. In this paper, we propose a joint measure, named joint probability score (JPS), to account for tracking error, misclassification and their interdependence...
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