This paper presents an application of a pedestrian-detection system aimed at localizing potentially dangerous situations under specific urban scenarios. The approach used in this paper differs from those implemented in traditional pedestrian-detection systems, which are designed to localize all pedestrians in the area in front of the vehicle. Conversely, this approach searches for pedestrians in critical areas only. The environment is reconstructed with a standard laser scanner, whereas the following check for the presence of pedestrians is performed due to the fusion with a vision system. The great advantages of such an approach are that pedestrian recognition is performed on limited image areas, therefore boosting its timewise performance, and no assessment on the danger level is finally required before providing the result to either the driver or an onboard computer for automatic maneuvers. A further advantage is the drastic reduction of false alarms, making this system robust enough to control nonreversible safety systems

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Abstract-This paper describes an approach for pedestrian detection in infrared images. The developed system has been implemented on an experimental vehicle equipped with an infrared camera and preliminarily tested in different situations.It is based on the localization of warm symmetrical objects with specific size and aspect ratio; since also road infrastructures and other road participants may have such characteristics, a set of matched filters was added in order to reduce false detections. A final validation process, based on the human shape's morphological characteristics, is used to build the list of pedestrian appearing in the scene.No temporal correlation, nor motion cues are used in this first part of the project.

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This paper describes a modular tracking system designed to improve the performance of a pedestrian detector. The tracking system consists of two modules, a kabeler and a predictor. The former associates a tracking identifier to each pedestrian, keeping memory of the past history; this is achieved by merging the detector and predictor outputs combined with data about vehicle motion. The predictor, basically a Kahan filter, estimates the new pedestrian position by observing hu previous movements. Its output helps the labeler to improve the match between the pedestrians detected in the new frame and those observed in the previous shots (feedback). If a pedestrian is occluded by some obstacle for a short while, the system continues tracking its movement using motion parameters. Moreover, it is able to reaSsign the same tracking ID in case the occlusion disappears in a short time. This behavior helps to correct temporary mis-recognitions that occur when the detector fails. The system has been tested using a quantitative performance evaluation tool, giving promising results.

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This paper presents the TerraMax vision systems used during the 2007 DARPA Urban Challenge. First, a description of the different vision systems is provided, focusing on their hardware configuration, calibration method, and tasks. Then, each component is described in detail, focusing on the algorithms and sensor fusion opportunities: obstacle detection, road marking detection, and vehicle detection. The conclusions summarize the lesson learned from the developing of the passive sensing suite and its successful fielding in the Urban Challenge