Visual feature tracking based on PHD filter for vehicle detection

Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02)

Distributed pedestrian detection alerts based on data fusion with accurate localization

Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01, GRANT TRA2010-20225-C03-03, GRANT TRA 2011-29454-C03-02 and iVANET TRA2010-15645) and CAM through SEGVAUTO-II (S2009/DPI-1509)

Context aided fusion procedure for road safety application

Proceeding of: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, Germany, September 13-15, 2012.Road safety applications require the most reliable and trustable sensors. Context information plays also a key role, adding trustability and allowing the study of the interactions and the danger inherent to them. Vehicle dynamics, dimensions... can be very useful to avoid misdetections when performing vehicle detection and tracking (fusion levels 0 and 1). Traffic safety information is mandatory for fusion levels 2 and 3 by evaluating the interactions and the danger involved in any detection. All this information is context information that was used in this application to enhance the capacity of the sensors, providing a complete and multilevel fusion application. Present application use three sensors: laser scanner, computer vision and inertial system, the information given by these sensors is completed with context information, providing reliable vehicle detection and danger evaluation. Test results are provided to check the usability of the detection algorithm.This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01), Driver Distraction Detector System (GRANT TRA2011-29454-C03-02). CAM through SEGVAUTO (S2009/DPI-1509).Publicad

Context aided fusion procedure for road safety application

Proceeding of: 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, Germany, September 13-15, 2012. Road safety applications require the most reliable and trustable sensors. Context information plays also a key role, adding trustability and allowing the study of the interactions and the danger inherent to them. Vehicle dynamics, dimensions... can be very useful to avoid misdetections when performing vehicle detection and tracking (fusion levels 0 and 1). Traffic safety information is mandatory for fusion levels 2 and 3 by evaluating the interactions and the danger involved in any detection. All this information is context information that was used in this application to enhance the capacity of the sensors, providing a complete and multilevel fusion application. Present application use three sensors: laser scanner, computer vision and inertial system, the information given by these sensors is completed with context information, providing reliable vehicle detection and danger evaluation. Test results are provided to check the usability of the detection algorithm. This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010-20225-C03- 01), Driver Distraction Detector System (GRANT TRA2011-29454-C03-02). CAM through SEGVAUTO (S2009/DPI-1509). Publicad