ORUSSI – Monitoraggio intelligente del traffico stradale basato su analisi automatica di segnali video

On giugno 10th, 2012, posted in: Progetti by

The growing mobility of people and goods has a very high societal cost in terms of traffic congestion and of fatalities and injured people every year. The management of a road network needs efficient ways for assessment at minimal costs. Road monitoring is a relevant part of road management, especially for safety, optimal traffic flow and for investigating new sustainable transport patterns.

On the road side, there are several technologies used for collecting detection and surveillance information: sophisticated automated systems such as in-roadway or over-roadway sensors, closed circuit television (CCTV) system for viewing real-time video images of the roadway or road weather information systems for monitoring pavement and weather.

Current monitoring systems based on video lack of optimal usage of networks and are difficult to be extended efficiently.

Our project focuses on road monitoring through a network of roadside sensors (mainly cameras) that can be dynamically deployed and added to the surveillance systems in an efficient way. The main objective of the project is to develop an optimized platform offering innovative real-time media (video and data) applications for road monitoring in real scenarios. The project will develop a novel platform based on the synergetic bundling of current research results in the field of semantic transcoding, the recently approved standard Scalable Video Coding standard (SVC), wireless communication and roadside equipment.

Dataset: thanks to the involvement of Comune di Prato (a local municipality), we were able to collect a very wide dataset of video sequences that turned out to be key for the project activities. The dataset is made of more than 250 hours of recording taken on a well-travelled county road, with different lighting and weather conditions. From these video sequences we have extracted an image dataset of about 1250 vehicle images. This data set, available here, can be used to train a vehicle classifier.

Technical report

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