Visual target tracking is to find samples that have the same semantic information as the tracking target and pinpoint the position of the sample in the video. Recently

the deep classification model is used to extract the deep embedded features of the tracking target. However

since the deep classification model gives the same class of sample labels

it can easily lead to tracking and even failure. In order to solve this problem

we add the spatial location information of the sample to the deep classification model and propose a location-sensitive loss function. The proposed loss function not only inherits the characteristics of classification loss

but also sorts samples of the same label according to the spatial location information of samples. In other words

the loss function in this paper can also encourage the classification between classes and classes. Compared with the classification loss function

the loss function in this paper is more suitable for the task of target tracking. In this paper

OTB100 and VOT2016 were tested

the results show that this algorithm can achieve better tracking performance.