Zobrazeno 1 - 10 of 24 pro vyhledávání: '"Yogesh Golhar"'
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Akademický článek
Robust Pedestrian Detection and Path Prediction using Improved YOLOv5
Autor: Kamal Omprakash Hajari, Ujwalla Gawande, Yogesh Golhar
Publikováno v: ELCVIA Electronic Letters on Computer Vision and Image Analysis, Vol 21, Iss 2 (2022)
In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges due to differences in pedestrian postures and scales, backd
Externí odkaz: https://doaj.org/article/ec9eb6740a0e487387570caf1075590d
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3
Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map
Autor: Ujwalla Gawande Ujwalla Gawande, Kamal Hajari Kamal Hajari, Yogesh Golhar
Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, postur
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ba16f153da1cff9c631ee7cb93dfdbf5
https://doi.org/10.21203/rs.3.rs-2750031/v1
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4
SIRA: Scale illumination rotation affine invariant mask R-CNN for pedestrian detection
Autor: Ujwalla, Gawande, Kamal, Hajari, Yogesh, Golhar
Publikováno v: Applied Intelligence (Dordrecht, Netherlands)
In this paper, we resolve the challenging obstacle of detecting pedestrians with the ubiquity of irregularities in scale, rotation, and the illumination of the natural scene images natively. Pedestrian instances with such obstacles exhibit significan
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efa18c5c39e226f70299d971d010f5d0
https://doi.org/10.1007/s10489-021-03073-z
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9
Novel approach for pedestrian unusual activity detection in academic environment
Autor: Kamal Omprakash Hajari, Ujwalla Haridas Gawande, Yogesh Golhar
Publikováno v: IAES International Journal of Artificial Intelligence (IJ-AI). 11:1517
In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, ve
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb0fba785f54035b783614c24e7fa07e
https://doi.org/10.11591/ijai.v11.i4.pp1517-1524
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