As collaborative trackers based on traditional fusion strategy has poor robustness in complex environments, a novel adaptive interactive fusion tracking strategy based on the online updated transition probability matrix in a multiple model particle filter framework was proposed. Firstly, an iterative updating equation was obtained based on minimum mean square error estimation method based on the Bayes theory. Then, the numerical solution of the iterative equation was obtained by numerical integration algorithm. Finally, with the updated TPM and re-sampling technology, the adaptive interaction of prior state distributions for different trackers was achieved to guarantee the target state of transmitted particles with larger weights. Tracking experiments were performed in complex environments. The results demonstrate that the proposed adaptive interactive fusion strategy improves the correction function for Particle prior state and effectively avoids the ‘tracking drifting’ problem from error accumulation. So, the robustness of proposed collaborative tracker is more better than those single trackers or collaborative trackers based other fusion strategy.