Object dynamics from video clips using YOLO framework

Chung, Wei-Hsiang (Iwate Prefectural University, Faculty of software and Information Science, Iwate, Japan) ; Chakraborty, Goutam (Iwate Prefectural University, Faculty of software and Information Science, Iwate, Japan) ; Chen, Rung-Ching (Chaoyang University, Department of Information Management, Taichung, Taiwan) ; Bornand, Cédric (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

In this paper we present a real-time object detection in the warehouse, to predict collision arising from moving forklifts andraising alarm when necessary. There are many research using YOLO for real-time object detection like detecting person or cars for Advanced Driver Assistance System(ADAS). There are many cargoes in the warehouse. Employees need to collect them and deliver to other place. When the employees are driving the forklift with many cargoes or big shipments that may block there vision to see objects in front of them. The employee driving the forklift might not see cargoes stored at the corner while turning, causing accident. The system will identify objects in real-time, received through surveillance camera set at a height from where it can clearly capture required frames to predict collision. If the camera predict that there will be an imminent collision, it will sound the alarm.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
ReDS - Reconfigurable & embedded Digital Systems
Publisher:
Morioka, Japan, 23-25 October 2019
Date:
2019-10
Morioka, Japan
23-25 October 2019
Pagination:
6 p.
Published in:
Proceedings of 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 23-25 October 2019, Morioka, Japan
DOI:
ISBN:
978-1-7281-3821-3
Appears in Collection:

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 Record created 2020-01-07, last modified 2020-01-07

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