Real-time Hand Region Detection and Tracking using Depth Information


KIPS Transactions on Software and Data Engineering, Vol. 1, No. 3, pp. 177-186, Dec. 2012
10.3745/KTSDE.2012.1.3.177, Full Text:

Abstract

In this paper, we propose a real-time approach for detecting and tracking a hand region by analyzing depth images. We build a hand model in advance. The model has the shape information of a hand. The detecting process extracts out moving areas in an image, which are possibly caused by moving a hand in front of a camera. The moving areas can be identified by analyzing accumulated difference images and applying the region growing technique. The extracted moving areas are compared against a hand model to get justified as a hand region. The tracking process keeps the track of center points of hand regions of successive frames. For this purpose, it involves three steps. The first step is to determine a seed point that is the closest point to the center point of a previous frame. The second step is to perform region growing to form a candidate region of a hand. The third step is to determine the center point of a hand to be tracked. This point is searched by the mean-shift algorithm within a confined area whose size varies adaptively according to the depth information. To verify the effectiveness of our approach, we have evaluated the performance of our approach while changing the shape and position of a hand as well as the velocity of hand movement.


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Cite this article
[IEEE Style]
S. Joo, S. Weon and H. Choi, "Real-time Hand Region Detection and Tracking using Depth Information," KIPS Transactions on Software and Data Engineering, vol. 1, no. 3, pp. 177-186, 2012. DOI: 10.3745/KTSDE.2012.1.3.177.

[ACM Style]
Sungil Joo, Sunhee Weon, and Hyungil Choi. 2012. Real-time Hand Region Detection and Tracking using Depth Information. KIPS Transactions on Software and Data Engineering, 1, 3, (2012), 177-186. DOI: 10.3745/KTSDE.2012.1.3.177.