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.
Improved multispectral aggregate channel for pedestrian detection
First published at:Sep 15, 2017
Opto-Electronic Engineering Vol. 44, Issue 09, pp. 882 - 887 (2017) DOI:10.3969/j.issn.1003-501X.2017.09.004
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Get Citation: Peng Zhirong, Zhao Meirong, Yang Weiming, et al. Improved multispectral aggregate channel for pedestrian detection[J]. Opto-Electronic Engineering, 2017, 44(9): 882–887.
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