Whose Hands are These? Hand Detection and Hand-Body Association in the Wild

We study a new problem of detecting hands and finding the location of the corresponding person for each detected hand. This task is helpful for many downstream tasks such as hand tracking and hand contact estimation. Associating hands with people is challenging in unconstrained conditions since multiple people can be present in the scene with varying overlaps and occlusions. We propose a novel end-to-end trainable convolutional network that can Jointly detect hands and the body location for the corresponding person. Our method first detects a set of hands and bodies and uses a novel Hand-Body Association Network to predict association scores between them. We use these association scores to find the body location for each detected hand. We also introduce a new challenging dataset called BodyHands containing uncon-strained images with hand and their corresponding body locations annotations. We conduct extensive experiments on BodyHands and another public dataset to show the effectiveness of our method. Finally, we demonstrate the benefits of hand-body association in two critical applications: hand tracking and hand contact estimation. Our experiments show that hand tracking and hand contact estimation methods can be improved significantly by reasoning about the hand-body association. Code and data can be found at http://vision.cs.stonybrook.edu/supreeth/BodyHands/.