Omni-directional cameras which give 360 degree panoramic view of the surroundings have recently been used in many applications such as robotics, navigation and surveillance. This paper describes the application of parametric ego-motion estimation for vehicle detection to perform surround analysis using an automobile mounted camera. For this purpose, the parametric planar motion model is integrated with the transformations to compensate distortion in omni-directional images. The framework is used to detect objects with independent motion or height above the road. Camera calibration as well as the approximate vehicle speed obtained from CAN bus are integrated with the motion information from spatial and temporal gradients using Bayesian approach. The approach is tested for various configurations of automobile mounted omni camera as well as rectilinear camera. Successful detection and tracking of moving vehicles, and generation of surround map is demonstrated for application to intelligent driver support. Key words Motion estimation, Panoramic vision, Intelligent vehicles, Driver support systems, Collision avoidance 1

This paper describes a vehicle detection system using a single camera. It is based

Abstract–This paper will present an obstacle detection system that that relies on the 3D information provided by stereo reconstruction. The 3D features must be separated in road features and obstacle features. Instead of relying on the flatness of the road, the vertical road profile is modeled as a clothoid, and is estimated from the lateral projection of the 3D points. The points above the road are selected for grouping into objects, based on vicinity criteria and the variation of the point density with the distance. The resulted objects are used as measurements for a model-based tracking algorithm. The resulted system is a high-accuracy, far distance obstacle detector, able to function in a large variety of real-world scenarios. I.

Abstract—Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. Index Terms—ADAS, pedestrian detection, on-board vision, survey. Ç 1

As computer vision based systems like lane tracking, face tracking and obstacle detection mature an enhanced range of driver assistance systems are becoming feasible. This paper introduces a list of core competencies required for a driver assistance system, the issue of building in robustness is highlighted in contrast to leaving such considerations to a later product development phase. We then demonstrate how these issues may be addressed in driver assistance systems based primarily on computer vision. The underlying computer vision systems are discussed followed by an example of a driver support application for lane keeping based on force-feedback through the steering wheel. 1

On-line stereo calibration is useful in many situations where the cameras are moving relative to each other. The motion can either be intentional, as in an active stereo head, or due to vibrations, heat etc. which is commonly found in automotive applications. However, most approaches for finding the essential matrix relating the two cameras are computationally very expensive and, hence, this problem must be addressed. In this paper, we suggest deferring a large portion of the image processing onto a Field Programmable Gate Array (FPGA) since most operations can be heavily parallelized. The specific algorithm chosen to find point correspondences between the left and the right images is SIFT[1] which has the advantage of producing a very small number of outliers. Having few outliers is important as computing the essential matrix from point correspondences is an inherently unstable problem, particularly in the case where the cameras are nearly parallel. We present a system which computes the computationally intensive parts of SIFT (Gaussian pyramid, Sobel etc) using an FPGA. The host computer then uses the resulting point correspondences to estimate the essential matrix with the help of a reduced model of the camera setup. On-line stereo calibration at frame rate (60Hz) is then possible without excessively loading the host computer.

This paper presents a stereo vision system for the detection and distance computation of a preceding vehicle. It is divided in two major steps. Initially, a stereo vision-based algorithm is used to extract relevant three-dimensional (3-D) features in the scene, these features are investigated further in order to select the ones that belong to vertical objects only and not to the road or background. These 3-D vertical features are then used as a starting point for preceding vehicle detection; by using a symmetry operator, a match against a simplified model of a rear vehicle's shape is performed using a monocular vision-based approach that allows the identification of a preceding vehicle. In addition, using the 3-D information previously extracted, an accurate distance computation is performed.

University, participated in the DARPA Urban Challenge and was one of the 11 teams selected to compete in the final event. Through development, testing, and participation in the official events, we experimented and demonstrated autonomous truck operations in (controlled) urban streets of California, Wisconsin, and Michigan under various climate and traffic conditions. In these experiments TerraMax TM, a modified medium tactical vehicle replacement (MTVR) truck by Oshkosh Corporation, negotiated urban roads, intersections, and parking lots and interacted with manned and unmanned traffic while observing traffic rules. We accumulated valuable experience and lessons on autonomous truck operations in urban environments, particularly in the aspects of vehicle control, perception, mission planning, and autonomous behaviors, which will have an impact on the further development of large-footprint autonomous ground vehicles for the military.

Abstract: We are developing computer vision algorithms for sensing important terrain features as an aid to wheelchair navigation, which interpret visual information obtained from images collected by video cameras mounted to the wheelchair. This paper focuses specifically on a novel computer vision algorithm for detecting curbs and other negative obstacles (i.e. anything below the level of the ground, such as holes and drop-offs), which are important and ubiquitous features on and near sidewalks and other walkways. The algorithm we develop extracts as much information as possible from depth information obtained from stereo video cameras (i.e. pairs of cameras mounted close together); other information (e.g. monocular cues such as intensity edges) will be incorporated in the future. We demonstrate experimental results on typical sidewalk scenes.

An atmospheric visibility measurement system capable of quantifying the most common operating range of onboard exteroceptive sensors is a key parameter in the creation of driving assistance systems. This information is then utilized to adapt sensor operations and processing or to alert the driver that the onboard assistance system is momentarily inoperative. Moreover, a system capable of either detecting the presence of fog or estimating visibility distances constitutes in itself a driving aid. In this paper, we present a technique to estimate the mobilized visibility distance through use of onboard CCD cameras. This distance represents the distance to the most distant object on the road surface having a contrast above 5 %. This definition is very close to the definition of the meteorological visibility distance proposed by the International Commission on Illumination (CIE). Our method combines the computations of a depth map of the vehicle environment using stereovision and of local contrasts above 5 %. In this paper, both methods are described separately. Then, their combination is detailed. Our method is operative in every kind of meteorological conditions and is evaluated thanks to video sequences under sunny weather and foggy weather. 1