A large-scale dataset of in-the-wild images for hand detection and contact recognition.

Supreeth Narasimhaswamy1, Trung Nguyen2, Minh Hoai1,2

1Stony Brook University, Stony Brook, NY 11790, USA
2VinAI Research, Hanoi, Vietnam

This work is based on Detecting Hands and Recognizing Physical Contact in the Wild (NeurIPS 2020). We collect a large-scale dataset of unconstrained images and annotate hand locations and their contact states.

We annotate hands by quadrilateral bounding boxes and provide contact state annotations by recognizing the following four conditions, namely: (1) No-Contact: the hand is not in contact with any object in the scene; (2) Self-Contact: the hand is in contact with another body part of the same person; (3) Other-Person-Contact: the hand is in contact with another person; and (4) Object-Contact: the hand is holding or touching an object other than people. These conditions are not mutually exclusive, and a hand can be in multiple states. For each hand instance, we annotate the four contact states by answering Yes, No, or Unsure.

Our dataset has annotations for 20,516 images, of which 18,877 form the training set and 1,629 form the test set.


Sample data from ContactHands. We show the bounding box annotations in green color. To avoid clutter, we display contact states for only two hand instances per image. The notations NC, SC, PC, and OC denote No-Contact, Self-Contact, Other-Person-Contact, and Object-Contact. We highlight the contact state for a hand by red color. If a contact state is unsure, we highlight it in blue.


If you find our work useful, please consider citing:

        title={Detecting Hands and Recognizing Physical Contact in the Wild},
        author={Supreeth Narasimhaswamy and Trung Nguyen and Minh Hoai},
        booktitle={Advances in Neural Information Processing Systems},


The dataset (4.5G) can be dowloaded from VinAI Server and Google Drive.

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