DiVA: A Distributed Video Analysis framework applied to video-surveillance systems

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. San Miguel, J. Bescós, J. M. Martónez, and Á. García, "DiVA: A Distributed Video Analysis Framework Applied to Video-Surveillance Systems", in WIAMIS '08. Ninth International Workshop on Image Analysis for Multimedia Interactive Services, 2008, Klagenfurt (Germay), 2008, pp. 207 - 210.This paper describes a generic, scalable, and distributed framework for real-time video-analysis intended for research, prototyping and services deployment purposes. The architecture considers multiple cameras and is based on a server/client model. The information generated by each analysis module and the context information are made accessible to the whole system by using a database system. System modules can be interconnected in several ways, thus achieving flexibility. Two main design criteria have been low computational cost and easy component integration. The experimental results show the potential use of this system.This work is supported by Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, by the Spanish Government (TEC2007-65400 SemanticVideo), by the Comunidad de Madrid (S-050/TIC-0223 - ProMultiDis-CM), by the Consejería de Educación of the Comunidad de Madrid and by The European Social Fund

EVALUATION OF A COMPUTER VISION TRAFFIC SURVEILLANCE SYSTEM

This thesis presents an evaluation of the accuracy of a novel computer vision traffic sensor - developed by the Clemson University Electrical and Civil Engineering Departments - capable of collecting a variety of traffic parameters. More specific, the thesis examines how the camera height and distance from the travel way affects the accuracy. The details of the quantitative and qualitative evaluations used to validate the system are provided. The parameters chosen to evaluate were volume, vehicle classification, and speed. Experimental results of cameras mounted at heights of 20 and 30 feet and a lateral distance of 10 and 20 feet show accuracy as high as 98 percent for volume and 99 percent for vehicle classification. Results also showed discrepancies in speeds as low as 0.031 miles per hour. Some issues which affected the accuracy were shadows, occlusions, and double counting caused by coding detection errors