文章基本信息

标题：Thermal Infrared Human Recognition Based on Multi-scale Monogenic Signal Representation and Deep Learning
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作者：Yong Tan ; Wenjuan Yan ; Shijian Huang 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2020
卷号：47
期号：3
语种：English
出版社：IAENG - International Association of Engineers
摘要：the infrared human recognition is challengeable due to the factors including poor imaging quality, disturbance objects in surroundings, large variations of human poses and casual movements. In this paper, a novel human recognition method is proposed. Its critical components include a feature descriptor that is referred to as a histogram of oriented monogenic energy (HOME), and a deep learning network that is referred to as a deep brief network (DBN). The feature descriptor, which is formulated from the multi-scale monogenic signal representation (MMSR), provides discriminative representation of lines/edges of the human subjects of interest. The DBN learns multiple layers of abstraction of the feature and conducts accurate human and/or non-human classification. Experimental results validate the advantages of the proposed method in recognition accuracy and robustness to scenic changes as well, due to such factors including the discriminative representation of human cues, high-level understanding of the cues, and tightly architectural coupling between the feature and the classifier.
关键词：infrared human recognition;multi-scale monogenic signal representation;histogram of oriented monogenic energy;deep brief network;contrastive divergence;maximum likelihood estimator