Algorithm of target tracking based on mean shift with RBF neural network
Bin Zhou, Wang Junzheng, Mao Jiali · 2008
The limitation of Mean Shift algorithm under crossing occlusion is analyzed. To solve this problem, a new tracking algorithm using Mean Shift with RBF neural network is proposed. According to the formal information about the objectpsilas location, the iteration start position is found with RBF neural network. And the objectpsilas real center is calculated by Mean Shift algorithm. Experimental results show that the proposal algorithm is stable to solve the crossing occlusion problem, and the iteration number is reduced.