7,590 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
Efficient Version-Space Reduction for Visual Tracking
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. Sample selection, labeling and updating the
classifier is prone to various sources of errors that drift the tracker. We
introduce the use of an efficient version space shrinking strategy to reduce
the labeling errors and enhance its sampling strategy by measuring the
uncertainty of the tracker about the samples. The proposed tracker, utilize an
ensemble of classifiers that represents different hypotheses about the target,
diversify them using boosting to provide a larger and more consistent coverage
of the version-space and tune the classifiers' weights in voting. The proposed
system adjusts the model update rate by promoting the co-training of the
short-memory ensemble with a long-memory oracle. The proposed tracker
outperformed state-of-the-art trackers on different sequences bearing various
tracking challenges.Comment: CRV'17 Conferenc
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.Comment: To appear in CVPR 201
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. Our project page is publicly available at:
https://github.com/zhen-he/tracking-by-animationComment: CVPR 201
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
Thermo-visual feature fusion for object tracking using multiple spatiogram trackers
In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework
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