Improved multispectral aggregate channel for pedestrian detection

Abstract

To solve the problem of low pedestrian detection accuracy in a single band due to variability and complexity of external environment, an improved pedestrian detection algorithm based on multispectral aggregate channel feature is proposed. The aggregate channel features of visible images and infrared images are extracted, respectively. The pixel contrast rule is changed and the results are compared with the adaptive threshold. The im-proved central symmetric local binary pattern feature is added to the feature channels. Different filter banks are designed to filter the multispectral aggregate channel features. The classifier is trained to realize the multispectral pedestrian detection. Experiments show that the improved local binary pattern feature can describe the symmetry of pedestrians of infrared images better and the intermediate filter layer enriches the candidate feature pool. The algorithm can effectively detect pedestrians in various scenes and improve the pedestrian detection accuracy. Compared with the previous multispectral aggregate channel detection work, the algorithm reduces the log-average miss rate.