Masters Theses
Keywords and Phrases
Human Activity Recognition; LiDAR
Abstract
"Accurate and real-time monitoring of urban traffic and pedestrian activities is crucial for intelligent transportation systems (ITS) and smart cities. Traditional camera-based methods struggle with issues like lighting and privacy. This research leverages advanced three-dimension light detection and ranging (3D LiDAR) technology and computational frameworks to address these challenges, providing transformative solutions for urban traffic management and pedestrian safety. By strategically deploying elevated LiDAR sensors, detailed 3D point cloud data is captured, enabling precise monitoring of urban environments. Enhancements to LiDAR-based frameworks, such as fine-tuning the Point Voxel Region-Based Convolutional Neural Network (PV-RCNN), improve the detection of vehicles and pedestrians alongside the classification the pedestrians' activities. Additionally, integrating the Point Net architecture with Long Short-Term Memory (LSTM) networks allows for classifying pedestrian activities, identifying potential safety risks, and supporting proactive public health interventions. To address data scarcity, this research uses Blender simulations to generate comprehensive urban traffic scenarios, providing robust training data for high-performance detection and classification models. These advancements in 3D LiDAR technology and computational techniques enhance urban surveillance, combining PV-RCNN for precise object detection and the Point Net-LSTM framework for activity classification. By capturing and analyzing detailed 3D data, this research supports the development of safer and smarter cities, where pedestrian and vehicle interactions are continuously monitored and managed with high accuracy. The methodologies developed not only address current challenges in urban monitoring but also pave the way for future innovations in ITS. Integrating these technologies showcases their potential to revolutionize urban planning, enhance public safety, and support the seamless functioning of smart cities. Promoting efficient data acquisition through elevated and simulated LiDAR setups, this work offers scalable and sustainable solutions for urban monitoring"-- Abstract, p. iii
Advisor(s)
Alsharoa, Ahmad
Committee Member(s)
Zawodniok, Maciej Jan, 1975-
Esmaeelpour, Mina
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Computer Engineering
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2025
Pagination
x, 54 page
Note about bibliography
Includes_bibliographical_references_(pages 47-53)
Rights
©2024 Nawfal Guefrachi , All Rights Reserved
Document Type
Thesis - Open Access
File Type
text
Language
English
Thesis Number
T 12456
Recommended Citation
Guefrachi, Nawfal, "Advanced 3d Lidar-Based Systems For Urban Traffic And Pedestrian Monitoring: Integrating Elevated Lidar, Data Collection, And Deep Learning For Precise Detection And Activity Classification" (2025). Masters Theses. 8231.
https://scholarsmine.mst.edu/masters_theses/8231
