The existing one-stage regression networks can obtain the multi-level information through the fusion of multi-branch response maps. However, the algorithms for response map fusion are mostly based on a simple element-wise sum or a multiplication operation. In this paper, a novel tracking model that includes a novel response map fusion method based on bilinear convolutional neural network, is proposed. The proposed model can obtain position correlation and information interaction of response maps, which is useful for achieving more accurate target tracking. The proposed algorithm is tested on the OTB2013 benchmark. Results show that, a competitive performance can be achieved by using the proposed model, compared to the state-of-arts tracking models.