DocumentCode :
254356
Title :
Using k-Poselets for Detecting People and Localizing Their Keypoints
Author :
Gkioxari, Georgia ; Hariharan, Balaji ; Girshick, Ross ; Malik, Jagannath
Author_Institution :
Univ. of California, Berkeley, Berkeley, CA, USA
fYear :
2014
fDate :
23-28 June 2014
Firstpage :
3582
Lastpage :
3589
Abstract :
A k-poselet is a deformable part model (DPM) with k parts, where each of the parts is a poselet, aligned to a specific configuration of keypoints based on ground-truth annotations. A separate template is used to learn the appearance of each part. The parts are allowed to move with respect to each other with a deformation cost that is learned at training time. This model is richer than both the traditional version of poselets and DPMs. It enables a unified approach to person detection and keypoint prediction which, barring contemporaneous approaches based on CNN features, achieves state-of-the-art keypoint prediction while maintaining competitive detection performance.
Keywords :
feature extraction; object detection; object recognition; prediction theory; CNN features; DPM; competitive detection performance; deformable part model; deformation cost; ground-truth annotations; k-poselet; keypoint prediction; keypoints configuration; keypoints localization; object recognition; people detection; person detection; Deformable models; Detectors; Face; Feature extraction; Torso; Training; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
Type :
conf
DOI :
10.1109/CVPR.2014.458
Filename :
6909853
Link To Document :
بازگشت