Vision-based bicycle/motorcycle classification
Pattern Recognition Letters
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Feature-based tracking approach for detection of moving vehicle in traffic videos
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Shape Features of Overlapping Boundary for Classification of Moving Vehicles
International Journal of Computer Vision and Image Processing
Background modeling methods for visual detection of maritime targets
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
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The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy.