Localization and analysis of critical areas in urban scenarios

This paper presents an application of a pedestrian detection system aimed at localizing potentially dangerous situations in specific urban scenarios. The approach used in this work differs from the one implemented in traditional pedestrian detection systems, which are designed to localize all pedestrians appearing in the area in front of the vehicle. This application first locates critical areas in the urban environment, and then it searches for pedestrians in these areas only. The environment is reconstructed with a standard laser scanner system, while the following check for the presence of pedestrians is performed thanks to the fusion with a vision system. The great advantages of such an approach are that pedestrian recognition is performed on a very limited image area -therefore boosting its timing performance- and no assessment on the danger level is finally required before providing the result to either the driver or an on-board computer for automatic manoeuvres

IR Pedestrian Detection for Advanced Driver Assistance Systems

This paper describes a system for pedestrian detection in infrared images implemented and tested on an experimental vehicle. A specific stabilization procedure is applied after image acquisition and before processing to cope with vehicle movements affecting the camera calibration. The localization of pedestrians is based on the search for warm symmetrical objects with specific size and aspect ratio. A set of filters is used to reduce false detections. The final validation process relies on the human shapes morphological characteristics. Document type: Part of book or chapter of boo

Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision

Autonomous driving in off-road environments requires an exceptionally capable sensor system, particularly given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a complex vision system, which is able to provide the two basic sensorial capabilities needed by autonomous vehicle navigation in extreme environments: obstacle detection and path detection. A variable-width-baseline (up to 1.5 m) single-frame stereo system is used for pitch estimation and obstacle detection, whereas a decision-network approach is used to detect the drivable path by a monocular vision system. The system has been field tested on the TerraMax vehicle, which is one of the only five vehicles to complete the 2005 Defense Advanced Research Projects Agency (DARPA) Grand Challenge course

Interim report on IR human localization research project

Rapporto tecnic

1st interim report on IR human localization research project.

Rapporto tecnic

Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation

In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, of low computational load, to calculate a V-disparity image between left and right corresponding images, in order to estimate the cameras pitch oscillation caused by the vehicle movement. Then, the obstacles are localized by stereo matching and mapped in real world coordinates. Experimental results on sequences taken from a moving vehicle (which partecipated to the DARPA Grand Challenge 2004) in different unstructured scenarios are then presented, to demonstrate the validity of the approach