Pedestrian dynamic and kinematic information obtained from vision sensors скачать в хорошем качестве

Pedestrian dynamic and kinematic information obtained from vision sensors

Paper The estimation and prediction of pedestrian motion is of fundamental importance in ITS applications. Most existing solutions have utilized a particular type of sensor for perception such as cameras (stereo, monocular, infrared) or other modalities such as a laser range finder or radar. The advent of wearable devices with inertial sensors have led to the development of systems capable of the robust inference of pedestrian intention. Unfortunately, these devices do not have communications capabilities to broadcast this information to all vehicles in proximity, and also this strategy requires functioning devices on all pedestrians to work. This paper presents a robust perception method that is able to extract dynamic pedestrian information with accuracy comparable to that of typical gyroscopes and accelerometers installed in wearable devices. Experimental results are presented to demonstrate the potential for obtaining very comprehensive dynamic information from limbs representing the skeleton of a pedestrian. This work also demonstrates the accuracy of vision based systems by comparing these results to the rotation and acceleration measured directly on the pedestrian using a wearable device. The contributions of this paper demonstrate that it is possible to significantly improve both the detection and estimation of pedestrian intention by incorporating dynamic information obtained from vision sensors. Title: "Pedestrian Dynamic and Kinematic Information Obtained from Vision Sensors" Authors: Santiago Gerling Konrad*; Mao Shan^; Favio R. Masson*; Stewart Worrall^ and Eduardo Nebot^ Published in: 2018 IEEE Intelligent Vehicles Symposium (IV) Link: https://ieeexplore.ieee.org/document/... Instituto de Investigaciones en Ingeniería Eléctrica, Departamento de Ingeniería Eléctrica y de Computadoras, Universidad Nacional del Sur (UNS)-CONICET, Bahía Blanca, Argentina. ^ Australian Centre for Field Robotics, The University of Sydney, Australia.