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http://dx.doi.org/10.3745/KTSDE.2013.2.10.713

Real-time Hand Region Detection based on Cascade using Depth Information  

Joo, Sung Il (숭실대학교 미디어학과)
Weon, Sun Hee (숭실대학교 미디어학과)
Choi, Hyung Il (숭실대학교 미디어학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.10, 2013 , pp. 713-722 More about this Journal
Abstract
This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.
Keywords
Hand Region Detection; Depth Image; Kinect; Adaboost; Depth Feature;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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