Fusion at Detection Level for Frontal Object Perception

International audienceIntelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects

Fusion at Detection Level for Frontal Object Perception

International audienceIntelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects

Computer vision techniques for forest fire perception

This paper presents computer vision techniques for forest fire perception involving measurement of forest fire properties (fire front, flame height, flame inclination angle, fire base width) required for the implementation of advanced forest fire-fighting strategies. The system computes a 3D perception model of the fire and could also be used for visualizing the fire evolution in remote computer systems. The presented system integrates the processing of images from visual and infrared cameras. It applies sensor fusion techniques involving also telemetry sensors, and GPS. The paper also includes some results of forest fire experiments.European Commission EVG1-CT-2001-00043European Commission IST-2001-34304Ministerio de Educación y Ciencia DPI2005-0229

Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

International audienceThe paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment

Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis

What Is Wrong with the No-Report Paradigm and How to Fix It

Is consciousness based in prefrontal circuits involved in cognitive processes like thought, reasoning, and memory or, alternatively, is it based in sensory areas in the back of the neocortex? The no-report paradigm has been crucial to this debate because it aims to separate the neural basis of the cognitive processes underlying post-perceptual decision and report from the neural basis of conscious perception itself. However, the no-report paradigm is problematic because, even in the absence of report, subjects might engage in post-perceptual cognitive processing. Therefore, to isolate the neural basis of consciousness, a no-cognition paradigm is needed. Here, I describe a no-cognition approach to binocular rivalry and outline how this approach can help resolve debates about the neural basis of consciousness