5,522 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
Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories
This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1
UV-Diagram: A Voronoi Diagram for Uncertain Spatial Databases
published_or_final_versio
Range Queries on Uncertain Data
Given a set of uncertain points on the real line, each represented by
its one-dimensional probability density function, we consider the problem of
building data structures on to answer range queries of the following three
types for any query interval : (1) top- query: find the point in that
lies in with the highest probability, (2) top- query: given any integer
as part of the query, return the points in that lie in
with the highest probabilities, and (3) threshold query: given any threshold
as part of the query, return all points of that lie in with
probabilities at least . We present data structures for these range
queries with linear or nearly linear space and efficient query time.Comment: 26 pages. A preliminary version of this paper appeared in ISAAC 2014.
In this full version, we also present solutions to the most general case of
the problem (i.e., the histogram bounded case), which were left as open
problems in the preliminary versio
Hierarchical Subquery Evaluation for Active Learning on a Graph
To train good supervised and semi-supervised object classifiers, it is
critical that we not waste the time of the human experts who are providing the
training labels. Existing active learning strategies can have uneven
performance, being efficient on some datasets but wasteful on others, or
inconsistent just between runs on the same dataset. We propose perplexity based
graph construction and a new hierarchical subquery evaluation algorithm to
combat this variability, and to release the potential of Expected Error
Reduction.
Under some specific circumstances, Expected Error Reduction has been one of
the strongest-performing informativeness criteria for active learning. Until
now, it has also been prohibitively costly to compute for sizeable datasets. We
demonstrate our highly practical algorithm, comparing it to other active
learning measures on classification datasets that vary in sparsity,
dimensionality, and size. Our algorithm is consistent over multiple runs and
achieves high accuracy, while querying the human expert for labels at a
frequency that matches their desired time budget.Comment: CVPR 201
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