CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TRAJECTORY-BASED SCENE DESCRIPTION AND CLASSIFICATION BY ANALYTICAL FUNCTIONS D. Pfeiffer, R. Reulke

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 TRAJECTORY-BASED SCENE DESCRIPTION AND CLASSIFICATION BY ANALYTICAL FUNCTIONS D. Pfeiffer 3 , R. Reulke b * 1 Akeleiweg 46D, 12487 Berlin, Germany - david@dltmail.de h Humboldt-University of Berlin, Computer Science Department, Computer Vision, Rudower Chaussee, Berlin, Germany - reulke@infonnatik.hu-berlin.de Commission III, WG III/4 and III/5 KEY WORDS: Object recognition, Scene Analysis, statistical and deterministic trajectory models ABSTRACT: Video image detection systems (VIDS) provide an opportunity to analyse complex traffic scenes that are captured by stationary video cameras. Our work concentrates on the derivation of traffic relevant parameters from vehicle trajectories. This paper examines dif ferent procedures for the description of vehicle trajectories using analytical functions. Derived conical sections (circles, ellipses and hyperboles) as well as straight lines are particularly suitable for this task. Thus, it is possible to describe a suitable trajectory’ by a maximum of five parameters. A classification algorithm uses these parameters and takes decisions on the turning behaviour of vehi cles. A model based approach is following. The a-priori knowledge about the scene (here prejudged and verified vehicle trajectories) is the only required input into this system. One confines himself here to straight lines, circles, ellipses and hyperboles. Other common functions (such as clothoids) are discussed and the choice of the function is being justified. 1. INTRODUCTION 1.1 Motivation Traffic management is based on an exact knowledge of the traffic situation. Therefore, traffic monitoring at roads and intersections is an essential prerequisite. Inductive loops and microwave radar systems are the most common detection and surveillance systems to measure traffic flow on public roads. VIDS that operate with real time image processing tech niques became more attractive during the last 15 years (Michalopoulos 1991), (Wigan 1992), (Setchell et al. 2001), (Kastrinaki et al. 2003). Traditional traffic parameters like presence, vehicle length, speed as well as time gap between two vehicles and vehicle classification (Wei et al. 1996) can be determined. In contrast to other sensors, the use of local cameras makes a two-dimensional observation possible and thus can determine new traffic parameters like congestion length, source-destination matrices, blockage or accidents and therefore support the estimation of travel times. Multi camera systems extend some limitations of single camera systems (e.g. occlusions, reliability) and enlarge the observa tion area (Reulke et al. 2008a). We proposed a framework that autonomously detects atypi cal objects, behavior or situations even in crowded and com plex situations (Reulke et al. 2008b). Extracted object data and object trajectories from multiple sensors have to be fused. An abstract situational description of the observed scene is obtained from the derived trajectories. The first step in describing a traffic scene is to ascertain the normal situa tion by statistical means. In addition, semantic interpretation is also derived from statistical information (such as direction and speed). Deviations of the inferred statistics are inter preted as atypical events, and therefore can be used to detect and prevent dangerous situations. These options allow the detection of sudden changes as well as atypical or threatening events in the scene. Atypical or threatening events are gener ally defined as deviations from the normal scene behavior or have to be defined by a rule based scheme. Red light runners and incident detection systems are an example for a self- evident road traffic application. The trajectories of street vehicles are smooth and homogene ous over a large scale. Therefore, a mathematical description by elementary functions is appropriate for these trajectories. Thus, dramatic reductions of the bandwidths are achieved for a full scene transmission. The basic step to determine the driver intentions is to fit the trajectories to the analytical functions. This paper is organized as follows: After an overview of situation analysis and atypical event detection the approach is introduced. Then, an example installation is described and its results are presented. The mathematical fundamentals of the adaptation of formerly derived trajectories of turning vehicles by hyperbolas, ellipsoids, spheres and straight lines are sketched. The derived information is very comprehensive but compact and permits downcast to other representations like source destination matrices. The paper closes with a sum mary and an outlook. 1.2 Situation Analysis and Atypical Event Detection Scene description and automatic atypical event detection are issues of increasing importance and an interesting topic in many scientific, technical or military fields where complex situations (i.e. scenes containing many objects and interac- Corresponding author.

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