ing at the problem of decreasing the recognition efficiency of multi-class Support Vector Machines (SVM) in the detection and classification of lane arrow markings, an improved method for a simple SVM classifier which is applied to realize the multi classification of arrow markings by custom binary encoding for results is proposed. The Harris corner coarseness is detected for the arrow markings region of interest (ROI), and the pseudo corners are screened by improved FAST-9 (features from accelerated segment test-9) algorithm. According to the location of the largest two corners of the ordinate in the final corner set, the recognition area is obtained. The SVM classifier is trained by invariant moments. And the multi classification with one SVM classifier is realized via the binary encoding for results. The algorithm is tested on 500 real images obtained from the real shot, and the recognition rate is superior to 96.8%. The results show that the proposed method does not need inverse perspective transformation. A simple SVM classifier can realize the multi classification of arrow markings, and the accuracy and operation efficiency of arrow marking recognition can be improved effectively.