Object tracking is one of the most important area of computer vision. In order to track the object whose appearance changes dramatically in complex scene

we propose a tracking algorithm based on the convolutional neural network. The network of our tracker has two parts: the feature extraction layer and the adaptive classifier layer. At the beginning

we train a fully-connected layer

a softmax layer and the linear relationship between feature and position of these samples. Next

we define a reliability of the tracking result. If the result is reliable

we will adjust the result location according to its features. Finally

in the network training process

we select the negative samples with max classifying scores in each iteration. The strategy could improve distinguishability of our tracker. Experiments on OTB50 show that our tracker could achieve state-of-the-art performance.