Abstract

We present an approach to the detection of parts of highly deformable ob jects, such as the human body. Instead of using kinematic constraints on relative angles used by most existing approaches for mod- eling part-to-part relations, we learn and use special observed ‘linking’ features that support particular pairwise part configurations. In addi- tion to modeling the appearance of individual parts, the current ap- proach 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 in- fer 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 evalu- ate 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.