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http://dx.doi.org/10.14372/IEMEK.2021.16.5.171

Pedestrian Recognition of Crosswalks Using Foot Estimation Techniques Based on HigherHRNet  

Jung, Kyung-Min (Sunmoon University)
Han, Joo-Hoon (Thinkwintek co.)
Lee, Hyun (Sunmoon University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.5, 2021 , pp. 171-177 More about this Journal
Abstract
It is difficult to accurately extract features of pedestrian because the pedestrian is photographed at a crosswalk using a camera positioned higher than the pedestrian. In addition, it is more difficult to extract features when a part of the pedestrian's body is covered by an umbrella or parasol or when the pedestrian is holding an object. Representative methods to solve this problem include Object Detection, Instance Segmentation, and Pose Estimation. Among them, this study intends to use the Pose Estimation method. In particular, we intend to increase the recognition rate of pedestrians in crosswalks by maintaining the image resolution through HigherHRNet and applying the foot estimation technique. Finally, we show the superiority of the proposed method by applying and analyzing several data sets covered by body parts to the existing method and the proposed method.
Keywords
Pose Estimation; Keypoint Detection; Crosswalk Pedestrian Recognition; Deep Learning;
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