Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Oba, Shigeyuki"'
Different layers in CNNs provide not only different levels of abstraction for describing the objects in the input but also encode various implicit information about them. The activation patterns of different features contain valuable information abou
Externí odkaz:
http://arxiv.org/abs/2010.01204
Employing one or more additional classifiers to break the self-learning loop in tracing-by-detection has gained considerable attention. Most of such trackers merely utilize the redundancy to address the accumulating label error in the tracking loop,
Externí odkaz:
http://arxiv.org/abs/1902.05211
The performance of an adaptive tracking-by-detection algorithm not only depends on the classification and updating processes but also on the sampling. Typically, such trackers select their samples from the vicinity of the last predicted object locati
Externí odkaz:
http://arxiv.org/abs/1806.02523
Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the collective knowledge of the committee. Committee members could vary in th
Externí odkaz:
http://arxiv.org/abs/1711.06564
A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize the target object. Such method relies on collective competence and the
Externí odkaz:
http://arxiv.org/abs/1704.08821
Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background.
Externí odkaz:
http://arxiv.org/abs/1704.00299
Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to update the object model. However, these approaches are heavily invest
Externí odkaz:
http://arxiv.org/abs/1704.00083
Publikováno v:
In Neural Networks August 2019 116:257-268
Publikováno v:
In Neural Networks September 2018 105:52-64
Publikováno v:
In Neural Networks March 2017 87:132-148