Keypoint recognition using randomized trees

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Keypoint recognition using randomized trees

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by Vincent Lepetit

Venue:	IEEE Trans. Pattern Anal. Mach. Intell
Citations:	215 - 17 self

BibTeX

@ARTICLE{Lepetit_keypointrecognition,
    author = {Vincent Lepetit},
    title = {Keypoint recognition using randomized trees},
    journal = {IEEE Trans. Pattern Anal. Mach. Intell},
    year = {},
    pages = {2006}
}

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Abstract

In many 3–D object-detection and pose-estimation problems, run-time performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a result, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in run-time computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, non-planar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.

Keyphrases

keypoint recognition training phase recognition performance pose-estimation problem scale variation classification problem frame-rate performance target object specific target object fast keypoint detector suffices run-time computational complexity repeatable result input image deformable object real-time system wide-baseline matching rigid one first contribution run-time performance keypoint-based approach second contribution computational burden critical importance large perspective repeatable object keypoints

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