113,331 research outputs found
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
Discriminative Scale Space Tracking
Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.Comment: To appear in TPAMI. This is the journal extension of the
VOT2014-winning DSST tracking metho
C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach
In many applications, maintaining a consistent dense map of the environment
is key to enabling robotic platforms to perform higher level decision making.
Several works have addressed the challenge of creating precise dense 3D maps
from visual sensors providing depth information. However, during operation over
longer missions, reconstructions can easily become inconsistent due to
accumulated camera tracking error and delayed loop closure. Without explicitly
addressing the problem of map consistency, recovery from such distortions tends
to be difficult. We present a novel system for dense 3D mapping which addresses
the challenge of building consistent maps while dealing with scalability.
Central to our approach is the representation of the environment as a
collection of overlapping TSDF subvolumes. These subvolumes are localized
through feature-based camera tracking and bundle adjustment. Our main
contribution is a pipeline for identifying stable regions in the map, and to
fuse the contributing subvolumes. This approach allows us to reduce map growth
while still maintaining consistency. We demonstrate the proposed system on a
publicly available dataset and simulation engine, and demonstrate the efficacy
of the proposed approach for building consistent and scalable maps. Finally we
demonstrate our approach running in real-time on-board a lightweight MAV.Comment: 8 pages, 5 figures, conferenc
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