16,288 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
Apparent sharpness of 3D video when one eye's view is more blurry.
When the images presented to each eye differ in sharpness, the fused percept remains relatively sharp. Here, we measure this effect by showing stereoscopic videos that have been blurred for one eye, or both eyes, and psychophysically determining when they appear equally sharp. For a range of blur magnitudes, the fused percept always appeared significantly sharper than the blurrier view. From these data, we investigate to what extent discarding high spatial frequencies from just one eye's view reduces the bandwidth necessary to transmit perceptually sharp 3D content. We conclude that relatively high-resolution video transmission has the most potential benefit from this method
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