2,129 research outputs found
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
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 invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201
Active Collaboration of Classifiers for Visual Tracking
Recently, discriminative visual trackers obtain state-of-the-art performance, yet they suffer in the presence of different real-world challenges such as target motion and appearance changes. In a discriminative tracker, one or more classifiers are employed to obtain the target/nontarget label for the samples, which in turn determine the target’s location. To cope with variations of the target shape and appearance, the classifier(s) are updated online with different samples of the target and the background. Sample selection, labeling, and updating the classifier are prone to various sources of errors that drift the tracker. In this study, we motivate, conceptualize, realize, and formalize a novel active co-tracking framework, step by step to demonstrate the challenges and generic solutions for them. In this framework, not only classifiers cooperate in labeling the samples but also exchange their information to robustify the labeling, improve the sampling, and realize efficient yet effective updating. The proposed framework is evaluated against state-of-the-art trackers on public dataset and showed promising results
Efficient Diverse Ensemble for Discriminative Co-Tracking
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 their features, memory update schemes, or training data,
however, it is inevitable to have committee members that excessively agree
because of large overlaps in their version space. To remove this redundancy and
have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another, covering the
version space with minimum overlaps. In this study, we propose an online
ensemble tracker that directly generates a diverse committee by generating an
efficient set of artificial training. The artificial data is sampled from the
empirical distribution of the samples taken from both target and background,
whereas the process is governed by query-by-committee to shrink the overlap
between classifiers. The experimental results demonstrate that the proposed
scheme outperforms conventional ensemble trackers on public benchmarks.Comment: CVPR 2018 Submissio
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