1 November 2016 Improved semi-supervised online boosting for object tracking
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101572Y (2016) https://doi.org/10.1117/12.2247211
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
The advantage of an online semi-supervised boosting method which takes object tracking problem as a classification problem, is training a binary classifier from labeled and unlabeled examples. Appropriate object features are selected based on real time changes in the object. However, the online semi-supervised boosting method faces one key problem: The traditional self-training using the classification results to update the classifier itself, often leads to drifting or tracking failure, due to the accumulated error during each update of the tracker. To overcome the disadvantages of semi-supervised online boosting based on object tracking methods, the contribution of this paper is an improved online semi-supervised boosting method, in which the learning process is guided by positive (P) and negative (N) constraints, termed P-N constraints, which restrict the labeling of the unlabeled samples. First, we train the classification by an online semi-supervised boosting. Then, this classification is used to process the next frame. Finally, the classification is analyzed by the P-N constraints, which are used to verify if the labels of unlabeled data assigned by the classifier are in line with the assumptions made about positive and negative samples. The proposed algorithm can effectively improve the discriminative ability of the classifier and significantly alleviate the drifting problem in tracking applications. In the experiments, we demonstrate real-time tracking of our tracker on several challenging test sequences where our tracker outperforms other related on-line tracking methods and achieves promising tracking performance.
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Yicui Li, Yicui Li, Lin Qi, Lin Qi, Shukun Tan, Shukun Tan, } "Improved semi-supervised online boosting for object tracking ", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101572Y (1 November 2016); doi: 10.1117/12.2247211; https://doi.org/10.1117/12.2247211
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