Thierry Chateau

QUAFF: efficient C++ design for parallel skeletons

We present QUAFF, a new skeleton-based parallel programming library. Its main originality is to rely on C++ template meta-programming techniques to achieve high efficiency. In particular, by performing most of skeleton instantiation and optimization at compile-time, QUAFF can keep the overhead traditionally associated to object-oriented implementations of skeleton-based parallel programming libraries very small. This is not done at the expense of expressivity. This is demonstrated in this paper by several applications, including a full-fledged, realistic real-time vision application.

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about the distance distributed according to four planes. Like a vehicle, a pedestrian constitutes, in the vehicle environment, an obstacle that must be detected, and located and then identified and tracked if necessary. To improve the robustness of pedestrian detection using a single laser sensor, a detection system based on the fusion of information located in the four laser planes is proposed. The method uses a nonparametric kernel-density-based estimation of pedestrian position of each laser plane. The resulting pedestrian estimations are then sent to a decentralized fusion according to the four planes. Temporal filtering of each object is finally achieved within a stochastic recursive Bayesian framework (particle filter), allowing a closer observation of pedestrian random movement dynamics. Many experimental results are given and validate the relevance of our pedestrian-detection algorithm with regard to a method using only a single-row laser-range scanner.

A benchmark dataset for outdoor foreground/background extraction

Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations with weather variations such as wind, sun or rain. Moreover, we propose some evaluation criteria and an associated free software to compute them from several challenging testing videos. The evaluation process has been applied for several state of the art algorithms like gaussian mixture models or codebooks.

Localization in urban environments: monocular vision compared to a differential GPS sensor

In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then a 3D reconstruction of the path and the environment is computed off line from the learning sequence. The 3D reconstruction is then used for computing the pose of the robot in real time (30 Hz) in autonomous navigation. Results from our localization method are compared to the ground truth measured with a differential GPS.

Vehicle trajectories evaluation by static video sensors

Metrology of vehicle trajectories has several applications in the field of road safety, particularly in dangerous curves. Actually, it is of great interest to observe trajectories of vehicles with the aim of designing a real time driver warning device in dangerous areas. This paper addresses the first step of a work with a video system placed along the road with the objective of vehicles position and speed estimation. This system has been totally developed for this project and can record simultaneously three cameras with 640 times 480 pixels up to 30 frames per second (fps) and rangefinder informations. The best contribution of this paper is an original probabilistic background subtraction algorithm, first step of a global method (calibration, tracking, ...) implemented to be able to measure vehicle trajectories. Kinematic GPS (in post-processing) has been extensively used to get ground truth

Towards an alternative GPS sensor in dense urban environment from visual memory

In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then the computer builds a map of the environment. This is done by first extracting key frames from the learning sequence. Then the epipolar geometry and camera motion are computed between key frames. Additionally, a hierachical bundle adjustment is used to refine the reconstruction. The map stored for the localization include the position odf the camera associated with each key frame as well as a set of interest points detected in the images and reconstructed in 3D. Using this map it is possible to compute the localization of the robot in real time during the automatic driving phase.

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.

MCMC Particle Filter for Real-Time Visual Tracking of Vehicles

This paper adresses real-time automatic tracking and labeling of a variable number of vehicles, using one or more still cameras. The multi-vehicle configuration is tracked through a Markov Chain Monte-Carlo Particle Filter (MCMC PF) method. We show that integrating a simple vehicle kinematic model within this tracker allows to estimate the trajectories of a set of vehicles, with a moderate number of particles, allowing frame-rate computation. This paper also adresses vehicle tracking involving occlusions, deep scale and appearance changes: we propose a global observation function allowing to fairly track far vehicles as well as close vehicles. Experiment results are shown and discussed on multiple vehicle tracking sequences. Though now only tracking light vehicles, the ultimate goal of this research is to track and classify all classes of road users, also including trucks, cycles and pedestrians, in order to analyze road users interactions.

Pedestrian detection method using a multilayer laserscanner: Application in urban environment

Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of pedestrian detection using a single laser sensor we propose here a detection system based on the fusion of information located in the 4 laser planes. In this paper, we propose a Parzen kernel method that allows first to isolate the ldquopedestrian objectsrdquo in each plane and then to carry out a decentralized fusion according to the 4 laser planes. Finally, to improve our pedestrian detection algorithm we use a MCMC based PF method allowing a closer observation of pedestrian random movement dynamics. Many experimental results validate and show the relevance of our pedestrian detection algorithm in regard to a method using only a single-row laser-range scanner.

A guidance-assistance system for agricultural vehicles

Abstract This article presents a guidance-assistance system for agricultural machines. It is based on analysis of the vehicle’s environment by image processing to deduce a control law in the image space. Two algorithms of image processing and two control laws are presented. It presents some results of crop edge detection and control of the vehicle’s trajectory in several conditions. These results show the feasibility of such a project and the necessity to know the reliability of the image processing results to secure the viability of the complete system.