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Applying a Deep Learning Neural Network to Gait-Based Pedestrian Automatic Detection and Recognition

التفاصيل البيبلوغرافية
العنوان: Applying a Deep Learning Neural Network to Gait-Based Pedestrian Automatic Detection and Recognition
المؤلفون: Chih-Lung Lin, Kuo-Chin Fan, Chin-Rong Lai, Hsu-Yung Cheng, Tsung-Pin Chen, Chao-Ming Hung
المصدر: Applied Sciences, Vol 12, Iss 9, p 4326 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: artificial intelligence, convolution neural network, FlowNet, pedestrian recognition, Wide ResNet, YOLO, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Gait recognition is a noncontact biometric procedure that determines the identity or health status of a person by analyzing his or her walking posture and habits, including skeletal and joint movements. The most remarkable feature of this method is the possibility of conducting recognition without demanding much cooperation from participants. Therefore, this recognition technique has attracted much attention from scholars. Additionally, because of the rapid development of graphics processing unit technology, related hardware and computation performance, the applications of deep-learning technology are considerably enhanced. The objective of this study was to apply a deep neural network (DNN), which employs deep-learning technology, to achieve gait-based automatic pedestrian detection and recognition. In contrast to using wearable devices to precisely capture skeletal and joint movements, pedestrian color-image sequences were used as input in this study. Subsequently, a pretraining convolutional neural network (CNN) was employed to capture pedestrian location and extract pedestrian dense optical flow to serve as concrete low-level feature inputs. Then, a finely-tuned DNN based on the wide residual network was employed to extract high-level abstract features. In addition, to overcome the difficulty of obtaining local temporal features by using a 2D CNN, part of the 3D convolutional structure was introduced into the CNN. This design enabled use of limited memory to acquire more effective features and enhance the DNN performance. The experimental results show that the proposed method has exceptional performance for pedestrian detection and recognition.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/12/9/4326; https://doaj.org/toc/2076-3417
DOI: 10.3390/app12094326
URL الوصول: https://doaj.org/article/c25b18b0b47247e1ac8f6fdbb9f28858
رقم الأكسشن: edsdoj.25b18b0b47247e1ac8f6fdbb9f28858
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app12094326