基于SURF特征跟踪的动态手势识别算法

Dynamic Hand Gesture Recognition Based on SURF Tracking

提出了一种基于加速鲁棒特征(SURF)跟踪的动态手势识别算法.其特征在于算法无需预先检测分割人手区域,仅通过跟踪统计相邻帧间匹配SURF特征点的移动主方向来刻画手势运动轨迹.提出采用经时间规整的轨迹方向数据流来建立动态手势模型,利用基于相关分析的数据流聚类方法实现动态手势的识别,大大提高动态手势识别速度.实验使用26个英文字母作为动态手势训练和识别,手势训练集和测试集的识别率分别为87.1%和84.6%,并成功用于实验室自主研制的侦察移动机器人Hunter的运动控制中,证实了该方法的有效性.

A method of dynamic hand gesture recognition based on SURF（speeded up robust feature） tracking is proposed. The main characteristic is that the hand trajectory is described only by tracking the dominant movement directions of matched SURF points in adjacent frames with no need of the previous detection and segmentation of the hand region.The dynamic hand gesture is then modeled by a series of trajectory direction data streams after time warping.Accordingly,the data stream clustering method based on correlation analysis is developed to recognize a dynamic hand gesture and to speed up calculation. The proposed algorithm is tested on 26 alphabetic hand gestures and yields a satisfactory recognition rate,which is 87.1% on the training set and 84.6%on the testing set.Its successful application to the motion control of our self-developed robot Hunter also establishes the effectiveness of the approach.