关键词: 目标跟踪, 卷积神经网络, 粒子滤波, 模板更新

Abstract: An algorithm of adaptive particle filter target tracking based on depth feature and template updating is proposed in view of the problem that the accuracy of the target template update is not high, resulting in poor tracking algorithm robust problem, due to the influence of factors such as complex background, illumination and target scale change. Firstly, the tracking target is done affine transformation, and then the target template and the candidate template are obtained by the 12-layers convolution neural network constructed to extract the depth feature of the tracking target and its affine transformation, and the candidate template library is constructed on this account. Secondly, using the particle filter tracking algorithm, the prediction target is matched with the template in the candidate template library, determining the new target template and updating adaptively the candidate template library. The experimental results show that the algorithm can track the target stably under the bad conditions of occlusion, illumination, scale change, target-rotated and complex background. Compared with the other seven adv-anced algorithms in eighteen sets of test videos, the algorithm has higher target tracking precision and stronger robustness.

Key words: target tracking, convolution neural network, particle filter, template update