关键词: 卷积神经网络, TLD算法, 手势跟踪, HOG特征, SURF特征

Abstract: Aiming at the problem that Tracking-Learning-Detection(TLD) algorithm can not track failure under the conditions 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.

Key words: convolution neural network, Tracking-Learning-Detection（TLD） algorithm, gesture tracking, HOG feature, SURF feature