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ECCV
2010
Springer

Recursive Coarse-to-Fine Localization for fast Object Detection

8 years 11 months ago
Recursive Coarse-to-Fine Localization for fast Object Detection
Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and...
Marco Pedersoli, Jordi Gonzàlez, Andrew D. Bagdan
Added 03 Sep 2010
Updated 03 Sep 2010
Type Conference
Year 2010
Where ECCV
Authors Marco Pedersoli, Jordi Gonzàlez, Andrew D. Bagdanov, Juan José Villanueva
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