First-Person Pose Recognition Using Egocentric Workspaces
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2015)
摘要
We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint priors and depth-based features are crucial for robust performance. In egocentric views, hands and arms are observable within a well defined volume in front of the camera. We call this volume an egocentric workspace. A notable property is that hand appearance correlates with workspace location. To exploit this correlation, we classify arm+hand configurations in a global egocentric coordinate frame, rather than a local scanning window. This greatly simplify the architecture and improves performance. We propose an efficient pipeline which 1) generates synthetic workspace exemplars for training using a virtual chest-mounted camera whose intrinsic parameters match our physical camera, 2) computes perspective-aware depth features on this entire volume and 3) recognizes discrete arm+hand pose classes through a sparse multi-class SVM. We achieve state-of-the-art hand pose recognition performance from egocentric RGB-D images in real-time.
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关键词
first-person pose recognition,egocentric workspaces,3D pose estimation,upper limbs,chest mounted depth-camera,arm+hand configurations,global egocentric coordinate frame,synthetic workspace exemplar generation,virtual chest-mounted camera,physical camera,perspective-aware depth features,discrete arm+hand pose class recognition,sparse multiclass SVM,hand pose recognition,egocentric RGB-D images
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