融合Hu矩与BoF-SURF支持向量机的手势识别

基于尺度不变特征变换的特征包（BoF-SIFT）支持向量机的分类方法具有较好的手势识别效果, 但是计算复杂度高、实时性较差。为此, 提出了融合Hu矩与基于快速鲁棒特征的特征包（BoF-SURF）支持向量机（SVM）的手势识别方法。特征包模型中用快速鲁棒性特征（SURF）算法替换尺度不变特征变换（SIFT）算法提取特征, 提高了实时性, 并引入Hu矩描述手势全局特征, 进一步提高识别率。实验结果表明, 算法无论是实时性还是识别率都要高于BoF-SIFT支持向量机方法。

Hand gesture recognition based on combining Hu moments and BoF-SURF support vector machine

The classification method using bag of features-scale invariant feature transformation(BoF-SIFT) support vector machine got a better result on hand gesture recognition. However, it had a high computational complexity which results in the worse real-time performance. So, this paper proposed the method of combining the Hu moments and bag of features-speeded up robust feature (BoF-SURF) support vector machine. The SURF algorithm replacing the SIFT algorithm extracted the features in the bag-of-features model. It could enhance the real-time. Then, using the Hu moments described the global features of hand gesture image, it could enhance the recognition accuracy. The result shows that the real-time and accuracy of the proposed algorithm is higher than BoF-SIFT algorithm.