Laurent Malaterre

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

Automatic Detection and Parameter Estimation of Trees for Forest Inventory Applications Using 3D Terrestrial LiDAR

Forest inventory plays an important role in the management and planning of forests. In this study, we present a method for automatic detection and estimation of trees, especially in forest environments using 3D terrestrial LiDAR data. The proposed method does not rely on any predefined tree shape or model. It uses the vertical distribution of the 3D points partitioned in a gridded Digital Elevation Model (DEM) to extract out ground points. The cells of the DEM are then clustered together to form super-clusters representing potential tree objects. The 3D points contained in each of these super-clusters are then classified into trunk and vegetation classes using a super-voxel based segmentation method. Different attributes (such as diameter at breast height, basal area, height and volume) are then estimated at individual tree levels which are then aggregated to generate metrics for forest inventory applications. The method is validated and evaluated on three different data sets obtained from three different types of terrestrial sensors (vehicle-borne, handheld and static) to demonstrate its applicability and feasibility for a wide range of applications. The results are evaluated by comparing the estimated parameters with real field observations/measurements to demonstrate the efficacy of the proposed method. Overall segmentation and classification accuracies greater than 84 % while average parameter estimation error ranging from 1 . 6 to 9 % were observed.

Performance evaluation of 3D keypoint detectors for time-of-flight depth data

In object recognition techniques, specially feature-based methods, a fundamental step is to extract keypoints which are distinct and considerably interesting in the image. There are many different keypoint detectors already available, each with its own specific use and results vary enormously. It is widely agreed that evaluation of feature detectors is important. To our knowledge there is no comparative study for the performance of keypoint detectors for only depth data from Time-Of-Flight (ToF) camera. As ToF sensors are cheap and extensively used for robotic applications, especially sensors with low sensor noise like Swiss Ranger SR-4k which give only depth data, need arises for this comparative study. A meticulous acquisition of different household objects depth data has been achieved using a Cartesian robot. The pose information from the robot has been used for more accurate evaluation. Different keypoints valid for only depth data are extracted and their repeatability is evaluated. A Comparative study has also been done on standard RGB-D datasets using the new metrics we have defined, to test the correctness of our approach with state of the art approaches which have used RGB-D data.

Automatic detection of vehicles at road intersections using a compact 3D Velodyne sensor mounted on traffic signals

Real-time traffic monitoring can play an important role in efficient traffic management and increasing road capacity. In this paper, we present a new method for automatic detection of vehicles using a compact 3D Velodyne sensor mounted on traffic signals in the urban environment. Different aspects of the new Velodyne sensor are first studied and its data are characterized for its effective utilization for our application. The sensor is then mounted on top of a traffic signal to detect vehicles at road intersections. The 3D point cloud obtained from the sensor is first over-segmented into super-voxels and then objects are extracted using a Link-Chain method. The segmented objects are then detected/classified as vehicles or non-vehicles using geometrical models and local descriptors. The results evaluated on real data not only demonstrate the efficacy but also the suitability of the proposed solution for such traffic monitoring applications.

A Three-Dimensional Spatiotemporal Sensor by Means of a Heterogeneous Biaxial Camera System

The capabilities of a biaxial camera system to verify control loops are investigated here with the verification of a Cartesian robot. The biaxial camera system will serve in the future as a tool to investigate the three-dimensional trajectories of insects, projectiles and other airborne devices. Three-dimensional motion is measured by means of two cameras arranged at right angles to each other with a common focal point. Control loop instructions to the robot allow it to simulate a circular orbit. The control system for the Cartesian robot is documented. The biaxial camera system allows an elliptical least-squares minimization algorithm to be employed to measure the phenomenon produced by the Cartesian robot.