For the larger intra class variance and inadequate real time problems caused by factors of imaging scales difference in nighttime pedestrian detection. The paper designs a rapid dentify program for nighttime pedestrians based on entropy weight and header checksum of FCSVM optimization under the the application of the statistical learning principles. The program utilizes entropy weighted to improve the feature of gradient histogram, introduces three branch structure SVM to identify the target further,and uses rapid classification FCSVM to reduce the overhead required of computational and to ensure real time, finally through the header checksum method to analysis and assess error detection goals, to further improve the accuracy of image matching. Experimental results show that the scheme can distinguish far infrared pedestrian goals effectively at night environment, and have good usability in urban,suburban and other different application environments on the basis of ensuring pedestrian real time fully.
