V
Vijay Paidi
Researcher at Dalarna University
Publications - 14
Citations - 165
Vijay Paidi is an academic researcher from Dalarna University. The author has contributed to research in topics: Parking lot & Engineering. The author has an hindex of 4, co-authored 12 publications receiving 89 citations. Previous affiliations of Vijay Paidi include Indian Institute of Management Ahmedabad.
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Journal ArticleDOI
Smart parking sensors, technologies and applications for open parking lots: a review
TL;DR: A combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions is suggested.
Journal ArticleDOI
Deep learning-based vehicle occupancy detection in an open parking lot using thermal camera
TL;DR: Yolo, Yolo-conv, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time and are suitable for real-time detection while Resnet50 was computationally expensive.
Journal ArticleDOI
Using geospatial technology to strengthen data systems in developing countries: The case of agricultural statistics in India
TL;DR: In this paper, the authors used data from an extensive deployment of geospatial technology in India to compare crop areas estimated using Geospatial Technology to conventional methods and assess the differences between the methods.
Journal ArticleDOI
CO2 Emissions Induced by Vehicles Cruising for Empty Parking Spaces in an Open Parking Lot
TL;DR: In this article , a thermal camera was utilized to collect videos during peak and non-peak hours to estimate CO2 emissions and cruising distances observed at an open parking lot, and these trajectories were analyzed to identify optimal and nonoptimal cruising, time to park, and walking distances of drivers.
Proceedings ArticleDOI
Parking Occupancy Detection Using Thermal Camera
Vijay Paidi,Hasan Fleyeh +1 more
TL;DR: Pre-trained vehicle detection algorithms, Histogram of Oriented Gradient detectors, Faster Regional Convolutional Neural Network (FRCNN) and modified Faster RCNN deep learning networks were implemented and produced better detection results compared to other detectors.