Scale Invariant Mask R-CNN for Pedestrian Detection
Gawande, Ujwalla H. (Yeshwantrao Chavan College of Engineering (Maharashtra, Índia). Department of Information Technology)
Hajari, Kamal Omprakash (Yeshwantrao Chavan College of Engineering (Maharashtra, Índia). Department of Information Technology)
Golhar, Yogesh (R. H. Raisoni College of Engineering (Maharashtra, Índia). Department of Computer Science and Engineering)

Data:	2020
Resum:	Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrianshelps in various utility applications such as event detection in overcrowded areas, gender, and gaitclassification, etc. In this domain, the most recent research is based on instance segmentation using MaskR-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying aperson. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, wherethe background changing. In this paper, a combined approach of scale-invariant feature map generationfor detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection toovercome this drawback. The new database was created by recording the behavior of the student at theprominent places of the engineering institute. This database is comparatively new for pedestrian detectionin the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newlycreated database and has been compared with the Caltech [1], INRIA [2], MS COCO [3], ETH [4], andKITTI [5] database. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Convolutional neural network ; Instance segmentation ; Pedestrian Detection ; Mask R-CNN
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 19 Núm. 3 (2020) , p. 98-118 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v19-n3-gawande-hajari-golhar
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/375823
DOI: 10.5565/rev/elcvia.1278

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