A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
How to protect privacy in floating car data systems
Proceedings of the fifth ACM international workshop on VehiculAr Inter-NETworking
Intelligent extended floating car data collection
Expert Systems with Applications: An International Journal
Real-Time Vision-Based Vehicle Detection for Rear-End Collision Mitigation Systems
Computer Aided Systems Theory - EUROCAST 2009
Index coded repetition-based MAC in vehicular ad-hoc networks
CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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This paper presents a complete vision-based vehicle detection system for floating car data (FCD) enhancement in the context of vehicular ad hoc networks (VANETs). Three cameras (side-, forward- and rear-looking cameras) are installed onboard a vehicle in a fleet of public buses. Thus, a more representative local description of the traffic conditions (extended FCD) can be obtained. Specifically, the vision modules detect the number of vehicles contained in the local area of the host vehicle (traffic load) and their relative velocities. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision modules with the data supplied by the CAN Bus and the GPS sensor. This information is transmitted by means of a GPRS/UMTS data connection to a central unit which merges the extended FCD in order to maintain an updated map of the traffic conditions (traffic load and average road speed). The presented experiments are promising in terms of detection performance and computational costs. However, significant effort is further necessary before deploying a system for large-scale real applications.