Compared with the common color video

infrared video is easily affected by the surrounding environment. In infrared pedestrian tracking

the appearance contour and gray distribution of the pedestrian target often have great changes

which lead to the difficulty of tracking. To solve this problem

this paper proposes a VPSiamRPN (Video prediction with Siamese Region Proposal Network) infrared pedestrian target tracking system. Aiming at the factors that seriously affect the performance of infrared pedestrian tracking (such as target deformation

target occlusion

and background clutter)

the image prediction function of PredNet (Deep Predictive Coding Networks for Video Prediction and Unsupervised) was designed and applied to SiamRPN (Siamese Region Proposal Network) to improve the similarity between the tracking template and the detected target

so as to improve the tracking ability to the infrared pedestrian target. Nine comparative experiments were carried out by changing the number of layers of the network

the number of target images and frames used for prediction

and the tracking strategy of the network. On PTB-TIR dataset

experimental results show that the success plots and precision of theinfrared target recognition in thermal crossover

intensity change

occlusion

scale variation

and other attributes were much higher than those of SiamRPN

indicating good performance of infrared pedestrian tracking

which will have broad application prospects in this field.