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

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 176 for example is explained in (Haag and Nagel, 1999), (Moon et al., 2002), (Hinz, 2004) and (Ernst et al., 2005). In (Haag and Nagel, 1999) a very extensive database of about 400 different three-dimensional car models is used to predict the appearance of vehicles including their shadow cast. In (Hinz, 2004) the au thor uses not only the shadow but additionally the luminance and reflectivity of the car’s surface as well which of course is more expensive to process. Next to shape and shadow in (Zhao and Nevatia, 2003) they try to recognize the windshield of vehicles. The final decision is made by a Bayesian Network. Most of them have a very reliable detection rate of more than 90 percent but a long computing time. In the papers (Moon et al., 2002) and (Ernst et al., 2005) they use rather simple two-dimensional mod els for detection. While in (Ernst et al., 2005) the authors search in the edge filtered image for rectangular objects of certain size in (Moon et al., 2002) they already shape the edge filter to a rect angle of expected car size. Both of them provide a fast and ac ceptable detection rate using additional information about street area and direction. The use of implicit models is explained in (Grabner et al., 2008) and (Lei et al., 2008). In (Grabner et al., 2008) the author sup poses to use a learning AdaBoost algorithm which is robust and fast by making a lot of cascaded weak decisions. In (Lei et al., 2008) they train a support vector machine with the SIFT descrip tors of selected cars and non-cars. But both of these approaches have to be trained with lots of positive and negative samples be fore working independently. Additionally it is not easy to cover all cases of illumination and environment. That’s why many lean ing algorithms have to be trained for every situation separately. Another easy approach for detection of moving cars without us ing any model is explained in (Reinartz et al., 2006) where they detect all moving objects in adjacent images by computing the normalized difference image. But as the georegistration of the images often is less exact than the pixel size, the images have to be coregistered first. On the other hand only moving objects can be detected while traffic jams or queues in front of a traffic light would be ignored. Concerning tracking there are lots of publications using optical flow and Kalman or particle filters to predict the expected dis placement and appearance in following images. (Haag and Nagel, 1999) and (Nejadasl et al., 2006) pursued this approach which is not easy to realize in the special case of only two or three adjacent images. In (Lenhart and Hinz, 2006) they use especially triplets of images to determine the best match between at least three states which can be described as a kind of prediction. Another good idea for the special case of very short bursts is presented in (Scott and Longuet-Higgins, 1991) and improved in (Pilu, 1997). The authors use singular value decomposition of a distance matrix to match a group of features to another one with respect to the rela tive positions of all features among each other. (Pilu, 1997) later extends the approach by adding the correlation between pairs of features. 2 APPROACH 2.1 Preprocessing To identify the active regions as well as the orientation of images among each other they have to be georeferenced, which means their absolute geographic position and dimension have to be de fined. Related to the GPS/IMU information and a digital terrain model the image data gets projected into GeoTIFF images, which are plane and oriented into north direction. This is useful to com bine the recorded images with existing datasets like maps or street data. To avoid examining the whole image data, only the street area given by a database is considered. 2.2 Detection For providing fast detection of traffic objects in the large images a set of modified edge filters, that represent a two-dimensional car model, is used. Recent tests showed that the car’s color in formation does not yield better results in detection than its gray value. Therefore the original images are converted into gray im ages. This conversion saves two thirds of filtering time. As there is additional information about street area and orientation this knowledge is used as well. The databases provided by Navteq (www.navteq.com) and Atkis (www.atkis.de) for example con tain that information about the street network. For every street segment covered by the image a bounding box around it is cut out. The subimage is masked with the street segment to only use the filters on traffic area. We use neither a Hough transforma tion for finding straight edges nor a filter in shape of the whole car, as mentioned in (Moon et al., 2002). But we create four spe cial shaped edge filters to represent all edges of the car model, which are elongated to the average expected size and turned into the direction given by the street database (fig.2 and 3). To Figure 3: The associated filter kernels Figure 4: The shifted and thresholded filter answers 2 and 3 avoid filtering for all different car sizes, we only shift the filter answers (fig.4) to the expected car edges within a certain range. This has the same effect as positioning the filter kernels around an anchor point. In the conjunction image of the four thresholded and shifted edge images remain blobs at the position, where all four filters have answered strong enough to the related edge filter. The regions remaining (fig.5) become thinned by a non-maxima

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