The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird's-eye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford RobotCar Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.Comment: equal contribution of first two authors, 8 full pages, 6 figures, accepted at IV 201

Ten Years of Pedestrian Detection, What Have We Learned?

Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark. We observe that there exist three families of approaches, all currently reaching similar detection quality. Based on our analysis, we study the complementarity of the most promising ideas by combining multiple published strategies. This new decision forest detector achieves the current best known performance on the challenging Caltech-USA dataset.Comment: To appear in ECCV 2014 CVRSUAD workshop proceeding

REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed