基于深度信息和SURF-BoW的中国手语识别算法<sup>*</sup>

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Abstract To realize the accurate recognition of sign language in the video, an algorithm based on depth image CamShift(DI_CamShift) and speeded up robust features-bag of words (SURF-BoW) is proposed. Kinect is used as the sign language video capture device to obtain both of the color video and depth image information of sign language gestures. Firstly, spindle direction angle and mass center position of the depth images are calculated and the search window is adjusted to track gesture. Next, an OTSU algorithm based on depth integral image is used for gesture segmentation, and the SURF features are extracted. Finally, SURF-BoW is built as the feature of sign language and SVM is utilized for recognition. The best recognition rate of single manual alphabet reaches 99.37%, and the average recognition rate is up to 96.24%.

Key words： Depth Image CamShift(DI_CamShift) Speeded Up Robust Features-Bag of Words(SURF-BoW) Depth Image Sign Language Recognition

Received: 24 April 2013

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	YANG Quan
	PENG Jin-Ye

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YANG Quan,PENG Jin-Ye. Chinese Sign Language Recognition Method Based on Depth Image Information and SURF-BoW[J]. , 2014, 27(8): 741-749.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I8/741

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