- Simple ideas feed research on multimedia and computer vision
I'm an interaction designer and an intelligent web applications developer. My research interests focus on machine learning, collective intelligence, rich internet applications, social networks analysis and the semantic web.
I'm a PhD student at University of Florence. My main research interests are focused on application of pattern recognition and computer vision specifically in the field of video-surveillance with PTZ cameras, local pose estimation and 2D/3D face pose estimation.
I’m currently a PhD student at University of Florence. My research interests are focused on application of pattern recognition and machine learning, computer vision specifically in the field of human activity recognition.
I'm working as assistant professor at the Dipartimento Sistemi e Informatica of the University of Florence. My research work is in the field of Computer Vision and Pattern Recognition, and I mostly work on automatic video analysis, annotation and semantic transcoding.
I'm a developer and an interaction designer. My work focus on natural interaction and multitouch surfaces, rich internet applications and the semantic web.
- Andrea Ferracani
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. Current monitoring systems based on video lack of optimal usage of networks and are difficult to be extended efficiently.
The ORUSSI 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. We exploit low-level efficient image features in order to enable our distributed system to extract semantic information from the imagery and to optimize the video compression adaptively.