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

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 42 tions) are observed and evaluated. A common aim is to de scribe the observed data and to detect atypical or threatening events. Other areas of situation analysis besides driver assistance (Reichardt 1995) may include traffic situation representation, surveillance applications (Beynon et al. 2003), sport video analysis or even customer tracking for marketing analysis (Leykin et al. 2005). (Kumar et al. 2005) developed a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. For behavior recognition, interactions between two or more mobile targets as well as between targets and stationary objects in the environment have been considered. The approach is based on sets of pre defined behavior scenarios, which need to be analyzed in different contexts. (Yung et al. 2001) demonstrate a novel method for automatic red light runner detection. It extracts the state of the traffic lights and vehicle motions from video recordings. 1.3 Image and Trajectory Processing The cameras deployed cover overlaid or adjacent observation areas. With it, the same road user can be observed using dif ferent cameras from different view positions and angles. The traffic objects in the image data can be detected using image processing methods. The image coordinates of these objects are converted to a common world coordinate system in order to enable the tracking and fusion of the detected objects of the respective observation area. High precision in coordinate transformation of the image into the object space is required to avoid mis- identification of the same objects that were derived from different camera positions. Therefore, an exact calibration (interior orientation) as well as knowledge of the position and view direction (exterior orientation) of the camera is neces sary. Since the camera positions are given in absolute geographical coordinates, the detected objects are also provided in world coordinates. The approach is subdivided into the following steps. Firstly, all moving objects have to be extracted from each frame of the video sequences. Secondly, these traffic objects have to be projected onto a geo-referenced world plane. Afterwards, these objects are tracked and associated to trajectories. One can now utilize the derived information to assess comprehen sive traffic parameters and to characterize trajectories of individual traffic participants. 1.4 Scenario The scenario has been tested at the intersection Rudower Chaussee / Wegedomstrasse, Berlin (Germany) by camera observation using three cameras mounted at a comer building at approximately 18 meters height. The observed area has an extent of about 100x100 m and contains a T-section. Figure 1 shows example trajectories derived from images taken from three different positions. The background image is an ortho photo, derived from airborne images. Figure 1. Orthophoto with example trajectories The aim is the description of the trajectories by functions with a limited number of parameters. Source destination matrices could be determined at these crossroads through such parameters without any further effort. A classification approach shall be used here. 2. PROCESSING APPROACH 2.1 Video Acquisition and Object Detection In order to receive reliable and reproducible results, only compact digital industrial cameras with standard interfaces and protocols (e.g. IEEE 1394, Ethernet) are deployed. Different image processing libraries or programs (e.g. OpenCV or HALCON) are available to extract moving ob jects from an image sequence. We used a special algorithm for background estimation, which adapts to the variable background and extracts the desired objects. The dedicated image coordinates as well as additional parameters like size and area were computed for each extracted traffic object. 2.2 Sensor Orientation The existing tracking concept is based on extracted objects, which are geo-referenced to a world coordinate system. This concept allows the integration or fusion of additional data sources. The transformation between image and world coor dinates is based on collinearity equations. The Z-component in world coordinates is deduced by appointing a dedicated ground plane. An alternative is the use of a height profile. Additionally needed input parameters are the interior and exterior orientation of the camera. For the interior orientation (principal point, focal length and additional camera distor tion) of the cameras the 10 parameter Brown distortion model (Brown 1971) was used. The parameters are being determined by a bundle block adjustment. Calculating the exterior orientation of a camera (location of the projection centre and view direction) in a well known world coordinate system is based on previously GPS meas ured ground control points (GCPs). The accuracy of the points is better than 5 cm in position and hight. The orienta tion is deduced through these coordinates using DLT and the spatial resection algorithm (Luhmann 2006).

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