Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Cascaded models for articulated pose estimation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parsing human motion with stretchable models
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We present an approach to the detection of parts of highly deformable objects, such as the human body. Instead of using kinematic constraints on relative angles used by most existing approaches for modeling part-to-part relations, we learn and use special observed 'linking' features that support particular pairwise part configurations. In addition to modeling the appearance of individual parts, the current approach adds modeling of the appearance of part-linking, which is shown to provide useful information. For example, configurations of the lower and upper arms are supported by observing corresponding appearances of the elbow or other relevant features. The proposed model combines the support from all the linking features observed in a test image to infer the most likely joint configuration of all the parts of interest. The approach is trained using images with annotated parts, but no a-priori known part connections or connection parameters are assumed, and the linking features are discovered automatically during training. We evaluate the performance of the proposed approach on two challenging human body parts detection datasets, and obtain performance comparable, and in some cases superior, to the state-of-the-art. In addition, the approach generality is shown by applying it without modification to part detection on datasets of animal parts and of facial fiducial points.