Human tracking across multiple cameras is highly demanded for large scale video surveillance. To successfully track human across multiple uncalibrated cameras that have no overlapping field of views, a system to train more reliable camera link models is proposed in this paper. We employ a novel approach of combining multiple camera links and building bidirectional transition time distribution in the process of estimation. Through the unsupervised scheme, the system builds several camera link models simultaneously for the camera network that has multi-path in presence of the outliers. Our proposed method decreases incorrect correspondences and results in more accurate camera link model for higher tracking accuracy. The proposed algorithm shows the effectiveness by evaluating in the real-world camera network scenarios.