Presented by: Benny
From: IEEE Transactions on Automation Science and Engineering 2013
Authors: Alex Teichman and Jake Lussier and Sebastian Thrun
Link: Paper
Abstract: We consider the problem of segmenting and tracking deformable objects in color video with depth (RGBD) data available from commodity sensors such as the Asus Xtion Pro Live or Microsoft Kinect. We frame this problem with very few assumptions - no prior object model, no stationary sensor, no prior 3D map - thus making a solution potentially useful for a large number of applications, including semi-supervised learning, 3D model capture, and object recognition.
Our approach makes use of a rich feature set, including local image appearance, depth discontinuities, optical flow, and surface normals to inform the segmentation decision in a conditional random field model. In contrast to previous work in this field, the proposed method learns how to best make use of these features from ground-truth segmented sequences. We provide qualitative and quantitative analyses which demonstrate substantial improvement over the state of the art.
This paper is an extended version of our previous work [29]. Building on this, we show that it is possible to achieve an order of magnitude speedup and thus real-time performance ( 20FPS) on a laptop computer by applying simple algorithmic optimizations to the original work. This speedup comes at only a minor cost in
overall accuracy and thus makes this approach applicable to a broader range of tasks. We demonstrate one such task: real-time, online, interactive segmentation to efficiently collect training data for an off-the-shelf object detector.
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