A Method for Detecting Hands Moving Objects from Videos

In this paper, we propose a novel method to recognize human actions of moving objects with their hands from video. Hand-object interaction plays a central role in human-object interaction, and the action of moving an object with the hand is also important as a reliable clue that a person is touching and affecting the object. To detect such specific actions, it is expected that detection model training and model-based detection can be made more efficient by using features designed to appropriately integrate different types of information obtained from the video. The proposed method focuses on the knowledge that an object moved by a hand shows movements similar to those of the forearm. Using this knowledge, our method integrates skeleton and motion information of the person obtained from the video to evaluate the difference in movement between the forearm region and the surrounding region of the hand, and detects the hand moving an object by determining whether the similar movements as the forearm occur around the hand from these differences.