Integrating Recognition and Reconstruction for Cognitive Traffic Scene Analysis from a Moving Vehicle

2006 ◽  
pp. 192-201 ◽  
Author(s):  
Bastian Leibe ◽  
Nico Cornelis ◽  
Kurt Cornelis ◽  
Luc Van Gool
Keyword(s):  
Scene Analysis ◽  
Moving Vehicle ◽  
Traffic Scene Analysis

Towards robust automatic traffic scene analysis in real-time

2002 ◽  
Author(s):  
D. Koller ◽  
J. Weber ◽  
T. Huang ◽  
J. Malik ◽  
G. Ogasawara ◽  
...  
Keyword(s):  
Real Time ◽  
Scene Analysis ◽  
Traffic Scene Analysis

Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

2018 ◽  
Vol 22 (5) ◽  
pp. 704-710 ◽  
Author(s):  
Robert Kerwin C. Billones ◽  
◽  
Argel A. Bandala ◽  
Laurence A. Gan Lim ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Computer Vision ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Volume Estimation ◽  
Public Utility ◽  
Scene Analysis ◽  
Volume Distribution ◽  
Distribution Analysis ◽  
Traffic Scene Analysis

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

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