Authors :
Ashish Katiyar; Seema
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/34a4mwzb
Scribd :
https://tinyurl.com/jt68za74
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR234
Abstract :
The surge in vehicle numbers on roads
contributes significantly to traffic congestion and
management challenges, particularly evident in
developing nations like India where the influx of cars
exceeds road and parking capacity. Addressing these
issues necessitates the implementation of sophisticated
parking management systems. This project focuses on
two key objectives: detecting vehicle occupancy within
marked parking slots and analyzing parking data. Using
the parking lot near IIT Kanpur main gate as a reference,
video data was collected for 14 consecutive days, enabling
the evaluation of vehicle occupancy and parking patterns.
Object detection algorithms such as Mask-RCNN
and YOLO-v5 were employed to identify occupied
parking spaces within the lot. Various methods, including
HAAR cascade-based classifiers, DNN-based systems
utilizing ResNet classifiers, and RCNN with IoU, were
tested for detecting vehicles within allotted slots. The data
collected was stored in CSV format for analysis.
This project aims to provide insights into detecting
parking space availability and analyzing parking data to
optimize time and fuel efficiency. In the Mask-RCNN
approach, pre-occupied spaces are denoted by red boxes,
while green boxes represent available parking spots.
Similarly, YOLOv5 was utilized to count cars in video
frames and identify available parking spaces. The YOLO
Annotation Toolbox facilitated the extraction of parking
space coordinates from recorded video frames, which
were then visualized in QGIS for further analysis.
Keywords :
Parking Management System, Object Detection Algorithms, Mask-RCNN, Yolov5, Data Analysis, Q GIS, Real-Time Data Handling, Computer Vision, Urban Mobility.
The surge in vehicle numbers on roads
contributes significantly to traffic congestion and
management challenges, particularly evident in
developing nations like India where the influx of cars
exceeds road and parking capacity. Addressing these
issues necessitates the implementation of sophisticated
parking management systems. This project focuses on
two key objectives: detecting vehicle occupancy within
marked parking slots and analyzing parking data. Using
the parking lot near IIT Kanpur main gate as a reference,
video data was collected for 14 consecutive days, enabling
the evaluation of vehicle occupancy and parking patterns.
Object detection algorithms such as Mask-RCNN
and YOLO-v5 were employed to identify occupied
parking spaces within the lot. Various methods, including
HAAR cascade-based classifiers, DNN-based systems
utilizing ResNet classifiers, and RCNN with IoU, were
tested for detecting vehicles within allotted slots. The data
collected was stored in CSV format for analysis.
This project aims to provide insights into detecting
parking space availability and analyzing parking data to
optimize time and fuel efficiency. In the Mask-RCNN
approach, pre-occupied spaces are denoted by red boxes,
while green boxes represent available parking spots.
Similarly, YOLOv5 was utilized to count cars in video
frames and identify available parking spaces. The YOLO
Annotation Toolbox facilitated the extraction of parking
space coordinates from recorded video frames, which
were then visualized in QGIS for further analysis.
Keywords :
Parking Management System, Object Detection Algorithms, Mask-RCNN, Yolov5, Data Analysis, Q GIS, Real-Time Data Handling, Computer Vision, Urban Mobility.
