F. Collange

Automatic guidance of agricultural vehicles using a laser sensor

This paper presents a method of automatic guidance of an agricultural vehicle in a structured environment (windrow harvester) or an iterative structured environment (combine harvester) using a laser sensor. The sensor parameters are estimated using a correlation based approach. A filter is incorporated so as to limit the perturbations caused by dust. The robustness of the guidance system can be increased by computing a reliability criterion from the estimate model. Vegetation volume and height are calculated and can be applied to control the vehicle velocity.

Road detection and vehicles tracking by vision for an on-board ACC system in the VELAC vehicle

The paper presents a method designed to detect and track vehicles on highway in a safety improvement purpose. The goal of this kind of system is to regulate the speed of a vehicle so as to respect safety distances relative to vehicles ahead. The method is exclusively based on monocular computer vision and uses two algorithms. The first one is able to locate the lane borders in the image, and to deduce the 3D shape of the road axis. The second algorithm detects, tracks and computes the 3D location of vehicles ahead by using fixed lights embedded on these vehicles. By combining the results of the two algorithms, a fusion step permits us to know were the most dangerous vehicle is, according to its position, speed and circulation lane. The method has been implemented on our experimental vehicle VELAC and the whole system operates in real time conditions.

Road Detection and Vehicle Tracking by Vision for Adaptive Cruise Control

This article deals first with a process designed to detect the circulation lane of a vehicle by onboard monocular vision. This detection process is based on a recursive updating of a statistical model of the lane obtained by a training phase. Once the lane has been located, a reconstruction algorithm computes the vehicle location on its lane and the three-dimensional shape of the road. Thereafter, the authors seek to detect and track vehicles situated in front of their vehicle and equipped with specific visual markers in order to achieve an accurate determination of their location and speed. By combining these various data, the most dangerous obstacle can be identified. Each of these three processes is described in detail, and significant examples are provided.

Motion analysis of an articulated locomotion model by video and telemetric data

Traditional techniques of human motion analysis use markers located on body articulations. The position of each marker is extracted from each image. Temporal and kinematic analysis is given by matching these data with a reference model of the human body. However, as human skin is not rigidly linked with the skeleton, each movement causes displacements of the markers and induces uncertainty in results. Moreover, the experiments are mostly conducted in restricted laboratory conditions. The aim of our project was to develop a new method for human motion analysis which needs non-sophisticated recording devices, avoids constraints to the subject studied, and can be used in various surroundings such as stadiums or gymnasiums. Our approach consisted of identifying and locating body parts in image, without markers, by using a multi-sensory sensor. This sensor exploits both data given by a video camera delivering intensity images, and data given by a 3D sensor delivering in-depth images. Our goal, in this design, was to show up the feasibility of our approach. In any case the hardware we used could facilitate an automated motion analysis. We used a linked segment model which referred to Winters model, and we applied our method not on a human subject but on a life size articulated locomotion model. Our approach consists of finding the posture of this articulated locomotion model in the image. By performing a telemetric image segmentation, we obtained an approximate correspondence between linked segment model position and locomotion model position. This posture was then improved by injecting segmentation results in an intensity image segmentation algorithm. Several tests were conducted with video/telemetric images taken in an outdoor surrounding with the articulated model. This real life-size model was equipped with movable joints which, in static positions, described two strides of a runner. With our fusion method, we obtained relevant limbs identification and location for most postures.

Location and relative speed estimation of vehicles by monocular vision

We consider the problems of perception of an adaptive cruise control system. The purpose of this type of system is to regulate the speed of our vehicle so as to respect safety distances relative to vehicles ahead. The work described concerns the detection, location and especially tracking, by monocular vision, of target vehicles equipped with virtual marks. We focus on precise determination of the position and relative speed of the target vehicles. We show an example of cooperation between road detection and obstacle detection. The methods presented are tested on real roads on our VELAC demonstration vehicle. We include results obtained by this means.

Visual Tracking by a Multisensorial Approach

Abstract The problem of road obstacle detection and tracking is addressed. The multisensorial approach is based on the use of a mixed camera/3D-sensor. The 3D-sensor is a laser range finder equiped with two mirrors to control the laser beam direction. The multisensor is endowed with two faculties which make it a smart active sensor. They are the controlled perception and the visual servoing which allow a close collaboration between the 3D-world and the intensity world. Obstacle detection is deduced from 3D-image analysis. Tracking combines intensity image processing and visual servoing. Obstacle dynamical state is obtained by a Kalrnan filter.