针对实时、大词汇集、连续的手语视频高效准确地识别,提出了一种基于压缩感知与加速稳健特征(SURF)的手语关键帧提取算法。利用压缩感知将手语视频降维成低维多尺度帧图像特征,通过自适应阈值完成子镜头分割,以处理大量的手语帧数据;运用SURF特征点完成特征匹配,绘制其间的相似度曲线进而提取关键帧。在前期预处理阶段,采用基于HSV空间自适应颜色检测提取手势区域。实验验证,由本文算法提取到的关键帧具有较高的准确性,且算法具备处理大量复杂数据的能力。

A key frame extraction algorithm of sign language based on compressed sensing and speed up robust features(SURF) feature is proposed to recognize the real-time, large vocabulary sets and continuous sign language videos efficiently and accurately. The sign language videos are reduced to the image features of low dimensional and multi-scale frame with compressed sensing. The segmentation of sub lens is completed by a adaptive threshold value, and a large number of sign language frame data are processed. We use SURF feature points to complete the feature matching, and the SURF frame similarity curve is drawn for extracting the key frames. In the pre-processing stage, we use the HSV space adaptive color detection to abstract the sign language area. Experimental results show that the key frames extracted by the proposed algorithm have high accuracy, and the proposed algorithm has the ability to process large amounts of complex data.