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:: FAST VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE BURSTS Karsten Kozempel and Ralf 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 FAST VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE BURSTS Karsten Kozempel and Ralf Reulke German Aerospace Center (DLR e.V.), Institute for Transportation Systems RutherfordstraBe 2 12489 Berlin karsten.kozempel@dlr.de, ralf.reulke@dlr.de KEY WORDS: aerial, image, detection, tracking, matching ABSTRACT: Caused by the rising interest in traffic surveillance for simulations and decision management many publications concentrate on auto matic vehicle detection or tracking. Quantities and velocities of different car classes form the data basis for almost every traffic model. Especially during mass events or disasters a wide-area traffic monitoring on demand is needed which can only be provided by airborne systems. This means a massive amount of image information to be handled. In this paper we present a combination of vehicle detection and tracking which is adapted to the special restrictions given on image size and flow but nevertheless yields reliable information about the traffic situation. Combining a set of modified edge filters it is possible to detect cars of different sizes and orientations with minimum computing effort, if some a priori information about the street network is used. The found vehicles are tracked between two consecutive images by an algorithm using Singular Value Decomposition. Concerning their distance and correlation the features are assigned pairwise with respect to their global positioning among each other. Choosing only the best correlating assignments it is possible to compute reliable values for the average velocities. 1 INTRODUCTION 1.1 Motivation The gathering of traffic information is a base for all kinds of traf fic modeling, simulation and prediction for tasks like emission reduction, efficient use of infrastructure or extension planing of the road network as well as the intervention and resource planing. Next to the use of inductive loops, Video Image Detection Sys tems (VIDS) have become a common alternative due to their low price as well as their simplicity and effort of installation. Further more inductive loops can’t cover the whole road network and a lot of data has to be estimated. Especially during mass events or disasters with huge congestions or road blocks, they can't yield reliable information. For this special purpose the German Aerospace Center (DLR e.V.) developed the ANTAR system for airborne traffic monitor ing on demand. During the soccer world cup 2006 it was success fully applied to gather traffic data and predict traffic situation in three German cities (Ruhe et al., 2007). Based on this the DLR is developing the ARGOS system for wide-area traffic monitoring (fig.l). It contains next to a radar system the 3K-Cam, a device of three digital cameras with 16 mega pixels each. Together they cover an area of 2,5 km x 0,7 km with a resolution of 20 cm at an altitude of 1000 m over ground. Additionally a GPS/IMU-unit is used to record positioning and orientation data for every image taken. Thereby the achieved image data gets orthorectified and georeferenced on-board which means that the images arriving the traffic detecting software can be used as map images with given orientation and scale. A fact that makes measuring distances and computing velocities less complex. In the first chapter the conditions related to the observation sys tem are explained as well as the published work on this area. The second chapter describes the used algorithms, a modified edge filter for fast vehicle detection and an extended singular value de composition concerning distances and correlations for tracking in very short sequences. After this the results with a few examples are presented. Finally a conclusion with considering possible fur ther research will close the paper. 48 Mpix camera system radar system ^ gigabit ethemet GPS/IMU data interpretation data recording data processing & storage Figure 1 : Traffic monitoring system ARGOS 1.2 Special conditions There are two special points to consider while developing de tection and tracking. It should be respected that the preprocessed images depending on their altitude over ground can be very large, in the shown case 25-30 mega pixels. That’s why the detecting algorithm should be rather fast than exact. Already the previous system ANTAR demonstrated that for an overview of the traffic situation a completeness of two thirds is acceptable. Due to the mentioned size of the images (original size is 16 mega pixels) they cannot be transmitted continuously. After a burst of a few images (2-4) the stream is cut to save them. Therefore it is not necessary to implement a complex tracking filter which needs a long period to adapt to the scene. 1.3 Related work A grand variety of approaches in vehicle detection as well as in object tracking has been released in the last years. Detection methods can be divided into two groups, depending on the kind of model being used. The use of explicit models

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