This paper describes the overall framework and components of an experimental platform for driver behavior monitoring based on driver's traffic violation records. This platform is composed of two separate subsystems: a driver assistance system based on road sign detection and recognition, and a traffic violation recording unit in which the vehicle is involved. The system provides drivers with their traffic violation records allowing them to visualize the spatial and temporal information of their traffic violation using the standard Google Earth tool. This feedback can be used to persuade drivers in changing their driving styles by instilling improved behavior. The paper covers firstly the description of the hardware architecture and then presents the developed functionalities.

This paper presents practical, high-level topological navigation tasks, making use of our general purpose landmark learning and detection system, which includes the possibility of reading text or icons inside detected landmarks. Room identification from inside, without any initialization, is achieved through its landmark signature. Room search along a corridor is done by reading the content of room nameplates placed around for human use; this allow the robot to take high-level decisions, and results in a higher integration degree of mobile robotics in real life.

Deformable models have been intensively studied in image analysis through the last decade, and are used for detection and recognition of flexible or rigid templates under diverse viewing conditions. Genetic algorithm (GA) based deformable models are used for generic visual landmark detection and interpretation. The developed system allows topologic localization and navigation using natural and artificial landmarks, exploiting deformable models' ability for handling landmark perspective variations. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented on this paper, showing the effectiveness and capacity of the landmark detection and reading system.

This paper describes a vision-based landmark recognition system for use with mobile robot navigation tasks. A search algorithm based on genetic techniques for pattern recognition in digital images is presented. The developed system allows the topologic localization of a mobile robot using natural and artificial landmarks. Text strings inside landmarks can be read and interpreted, if present. The resulting system was tested onboard a B21 mobile robot and proved useful. The presented experimental results show the effectiveness of the proposed algorithm.