Abstract: In order to realize the accurate recognition of manual alphabets in the sign language video, this paper presented a sign language recognition algorithm based on DI_CamShift (Depth Image CamShift) and SLVW (Sign Language Visual Word). First, it used Kinect to obtain the video and depth image information of sign language gestures. Second, it calculated spindle direction angle and mass center position of the depth images to adjust the search window for gesture tracking. Third, an Ostu algorithm based on depth integral image was applied to gesture segmentation, then the Scale Invariant Feature Transform (SIFT) features was extracted. Finally, it built the SLVW bag of words and used SVM for recognition. The best recognition rate of single manual alphabet can reach 99.67%, and the average recognition rate is 96.47%.

Key words: DI_CamShift, Sign Language Visual Word (SLVW), Kinect, depth image, Scale Invariant Feature Transform (SIFT), sign language recognition

摘要： 为了实现手语视频中手语字母的准确识别，提出了一种基于DI_CamShift和手语视觉单词（SLVW）的手语识别算法。首先采用Kinect获取手语字母手势视频及其深度信息；然后通过计算获得深度图像中手语手势的主轴方向角和质心位置，计算搜索窗口对手势跟踪；进而使用基于深度积分图像的Ostu算法分割手势并提取其尺度不变特征转换(SIFT)特征；最后构建SLVW词包并用支持向量机(SVM)进行识别。单个手语字母最好识别率为99.67%，平均识别率96.47%

关键词: DI_CamShift, 手语视觉单词, Kinect, 深度图像, 尺度不变特征转换, 手语识别