Search

The horizontal features of vehicle bottom and the vertical features of both sides of vehicle are selected, and the local features of gray scale and its gradient and variation, which are less affected by global gray scale, are extracted. The horizontal and vertical features of vehicle are fused respectively by applying weighted evidence theory, and the weight of each feature is adjusted according to different light intensity. For improving the realtimeness of identification, artificial fish swarm algorithm is applied, and a vehicle identification module is added to enhance the guiding ability of artificial fishes. Based on search results and further judgment by symmetric feature, the accurate positioning of front vehicle is realized. Experiment results show that the technique proposed can accurately identify front vehicles under different lighting conditions with good adaptability, accuracy and realtimeness. [ABSTRACT FROM AUTHOR]