NDLI: Deep Learning Approaches to Pedestrian Detection: State of the Art

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Deep Learning Approaches to Pedestrian Detection: State of the Art

Content Provider	Scilit
Author	Hajari, Kamal Gawande, Ujwalla Golhar, Yogesh
Copyright Year	2021
Description	Pedestrian or human detection and classification is a significant and challenging problem in the field of computer vision. In recent years, researchers have employed deep learning frameworks and models for pedestrian detection and classification. Nevertheless, these frameworks have their shortcomings. Obtaining straightforward comparisons and choosing a relevant deep learning framework is a wearisome task for researchers. To address these shortcomings, we present a dogmatic analysis of a state-of-the-art deep learning framework adopted for pedestrian detection along with real-time solicitudes and challenges. In this chapter, our contributions comprise 1) a well-organized comparison of an advanced deep learning framework utilized for pedestrian detection, 2) a comparison of resembled benchmark pedestrian datasets, 3) a unique pedestrian database that embraces students' behavior in educational institutions, and 4) a proposed case study of a scale invariant approach of pedestrian detection. Our investigation will provide a new research direction in the area of pedestrian detection for practitioners and research scholars. Book Name: Computing Technologies and Applications
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003166702-17&type=chapterpdf
Ending Page	321
Page Count	21
Starting Page	301
DOI	10.1201/9781003166702-17
Language	English
Publisher	Informa UK Limited
Publisher Date	2021-09-20
Access Restriction	Open
Subject Keyword	Book Name: Computing Technologies and Applications Deep Learning Pedestrian Detection Shortcomings Classification Behavior Models Approaches To Pedestrian
Content Type	Text
Resource Type	Chapter

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