A real-time tracking algorithm based on convolutional neural network（ CNN） is proposed

using strong capabilities of feature learning of CNN.In this algorithm

a generial rule between motion and appearance features of tracking target is obtained

by offline training on 2-channel CNN

difference of two adjacent frames is learned

directly by network regresses obtain prediction of location of the target and the confidence score. In case of no online update is required. Results of experiment on VOT2014 dataset show that performance of the proposed tracking algorithm achieves the state-of-the-art

at the same time

it operates at a very high speed of 90 frames-persecond

which show good realtime performance.