关键词: 改进高斯混合模型, 分数阶导数学习率, 目标跟踪算法, 加权似然跟踪, 期望值最大化

Abstract: The target tracking algorithm based on WLT and IGMM is proposed in view of the GMM algorithm’s poor adaptability while the background changes fastly and target are multiple. An adaptive Gauss mixture model involving fractional derivative learning rate is introduced to detect moving targets in real time. The target localization is achieved by maximizing its weighted likelihood in the video. Moreover, the algorithm handles scale and rotation changes of the mulitarget. Experimental results in VOT2014 dataset suggested proposed algorithm involving WLT and IGMM comparing current popular tracking algorithm in tracking accuracy is improved greatly. In respond to the changes in multi-scale, multi-angle multi-target tracking shows greater advantage.

Key words: Improved Gaussian Mixture Model（IGMM）, fractional derivative learning rate, target tracking algorithm, Weighted Likelihood Tracking（WLT）, expectation-maximization