Abstract

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

3D point clouds; dataset creation; Elevated LiDAR; pedestrians and vehicles detection; urban environment surveillance

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2024

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