Mobile terminal gesture recognition based on improved FAST corner detection
Mobile terminal gesture recognition based on improved FAST corner detection
复制标题
基于改进的FAST角点检测的移动终端手势识别
DOI：
10.1049/iet-ipr.2018.5959
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发表时间：
2019-02
影响因子：
2.3
通讯作者：
Meiyu Zhang
中科院分区：
文献类型：
--
作者：
Chengfeng Jian;Xiaoyu Xiang;Meiyu Zhang
移动终端的手势识别是一项极具挑战性的工作，不仅计算资源有限使得特征点识别变得复杂，而且复杂的背景也容易影响识别结果。本文提出了一种基于加速片段测试（FAST）拐角检测改进特征的手势识别方法。首先，为了消除复杂背景和光线的影响，通过背景相减和多色空间得到两帧图像的交点，实现手的检测；其次，为了提高算法的性能，根据指尖的特点，提出了一种结合反向传播神经网络（BPNN）的改进FAST角点检测方法。随后，采用非极大值抑制法对特征点进行筛选。最后，通过特征点匹配实现手势识别。实验结果表明，该方法在复杂背景下具有较强的抗干扰能力，具有良好的性能。
Mobile terminal gesture recognition is an extreme challenge, not only because its limited computing resource make it complicated to identify feature points but also the complex background can easily affect the recognition result. This study proposes a gesture recognition method based on improved features from accelerated segment test (FAST) corner detection. First, in order to eliminate the effects of complex background and light, the intersection of the two frame images is obtained through background subtraction and the multi-colour space to realise the detection of the hand. Second, in order to improve the performance of the algorithm, an improved FAST corner detection method combined with the back propagation neural network (BPNN) is proposed in accordance with the characteristics of fingertips. Subsequently, the feature points are screened by method of non-maximum suppression. Finally, gesture recognition is realised by matching feature points. Experimental results illustrate that this method has strong anti-interference ability in complex background, and it is good at performance.
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