计算机工程与应用2019，Vol.55Issue(9)：151-156,6.DOI:10.3778/j.issn.1002-8331.1801-0284
基于卷积神经网络优化TLD运动手势跟踪算法
Optimized Motion Gesture Tracking TLD Algorithm Based on Convolution Neural Network
摘要
Abstract
Aiming at the problem that Tracking-Learning-Detection(TLD) algorithm can not track failure under the condi-tions of uneven illumination variation, occlusion and tracking target ambiguity, an optimized motion gesture tracking TLD algorithm based on convolution neural network is proposed. The gesture is taken as the positive sample and the background is taken as the negative sample. HOG feature is obtained and put into the convolution neural network for training, the gesture detection classifier is gotten. The target gesture area is determined and the automatic recognition of gesture is achieved. TLD algorithm is then used to track and learn the hand gesture, and the positive and negative samples are estimated and detected in real time. At the same time, SURF feature matching is used to update the tracker. Experimental results show that the proposed algorithm can improve the tracking accuracy by 4.24% compared with the original TLD algorithm. This method enhances the tracking effect of motion gesture, which is more robust than the traditional tracking algorithm.关键词
卷积神经网络/TLD算法/手势跟踪/HOG特征/SURF特征Key words
convolution neural network/ Tracking-Learning-Detection(TLD)algorithm/ gesture tracking/ HOG feature/SURF feature分类
信息技术与安全科学引用本文复制引用
王民,李泽洋,王纯,石新源..基于卷积神经网络优化TLD运动手势跟踪算法[J].计算机工程与应用,2019,55(9):151-156,6.基金项目
住房和城乡建设部科学技术项目计划(No.2016-R2-045) （No.2016-R2-045）
陕西省自然科学基础研究资金(No.2014JM8343) （No.2014JM8343）
陕西省自然科学基金青年基金(No.2013JQ8003). （No.2013JQ8003）
