In order to solve the problem of single feature tracking failure under the condition of background illumination change and partial occlusion in the road, a vehicle tracking algorithm based on multi-feature fusion and Kalman prediction is proposed. Multi-feature fusion includes tracking the color, edge and texture of the vehicle, using color histogram to describe the color distribution, using Local Binary Patterns (LBP) with rotation invariance to describe the texture distribution, using the improved Canny operator to calculate the edge distribution information, establishing the feature fusion function, and using the average peak correlation energy to construct the best feature description of this tracking. When the feature matching between two adjacent frames is greater than the set threshold in the vehicle tracking process, occlusion and tracking interruption are determined. Kalman filter is used to predict the current position. Finally, different experiments show the effectiveness of the algorithm.