Robust pedestrian detection and path prediction using mmproved YOLOv5
Hajari, Kamal Omprakash (Yeshwantrao Chavan College of Engineering)
Gawande, Ujwalla (Yeshwantrao Chavan College of Engineering)
Golhar, Yogesh (St. Vincent Palloti College of Engineering and Technology)

Data:	2022
Resum:	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, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study.
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:	CNN ; Deep learning ; Pedestrian detection ; Tracking ; Path prediction ; Computer vision ; Yolov5
Publicat a:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 21 Núm. 2 (2022) , p. 40-61 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1538
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001009
DOI: 10.5565/rev/elcvia.1538

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