65,122 research outputs found
Planar Object Tracking in the Wild: A Benchmark
Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.Comment: Accepted by ICRA 201
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools in
computer vision. In this work, we study the interface between these two
distinct topics and obtain techniques to uncover hierarchical block structure
in symmetric matrices -- an important aspect in the success of many vision
problems. Our new algorithm, the incremental multiresolution matrix
factorization, uncovers such structure one feature at a time, and hence scales
well to large matrices. We describe how this multiscale analysis goes much
farther than what a direct global factorization of the data can identify. We
evaluate the efficacy of the resulting factorizations for relative leveraging
within regression tasks using medical imaging data. We also use the
factorization on representations learned by popular deep networks, providing
evidence of their ability to infer semantic relationships even when they are
not explicitly trained to do so. We show that this algorithm can be used as an
exploratory tool to improve the network architecture, and within numerous other
settings in vision.Comment: Computer Vision and Pattern Recognition (CVPR) 2017, 10 page
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