Joint Pose Estimator and Feature Learning for Object Detection

A new learning strategy for object detection is presented. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. Specifically, we train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators instead of the usual image features. This allows the learning process to select and combine various estimates of the pose with features able to implicitly compensate for variations in pose. We demonstrate that a detector built in such a manner provides noticeable gains on two hand video sequences and analyze the performance of our detector as these data sets are synthetically enriched in pose while not increased in size.


Publié dans:
2009 Ieee 12Th International Conference On Computer Vision (Iccv), 1373-1380
Présenté à:
IEEE International Conference on Computer Vision
Année
2009
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
ISBN:
978-1-4244-4419-9
Mots-clefs:
Laboratoires:




 Notice créée le 2010-02-26, modifiée le 2019-12-05

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