The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.
ABSTRACT:Research of automatic vehicle detection in aerial images has been done with a lot of innovation and constantly rising success for years. However information was mostly taken from a single image only. Our aim is using the additional information which is offered by the temporal component, precisely the difference of the previous and the consecutive image. On closer viewing the moving objects are mainly vehicles and therefore we provide a method which is able to limit the search space of the detector to changed areas. The actual detector is generated of HoG features which are composed and linearly weighted by AdaBoost. Finally the method is tested on a motorway section including an exit and congested traffic near Munich, Germany.
Commission III, WG III/2 KEY WORDS: 3D surface matching, 3D similarity transformation, strip adjustment, laser altimetry ABSTRACT: Systematic errors in point clouds acquired by airborne laser scanners, photogrammetric methods or other 3D measurement techniques need to be estimated and removed by adjustment procedures. The proposed method estimates the transformation parameters between reference surface and registration surface using a mathematical adjustment model. 3D surface matching is an extension of 2D least squares image matching. The estimation model is a typical Gauss-Markoff model and the goal is minimizing the sum of squares of the Euclidean distances between the contiguous surfaces. Besides the generic mathematical model, we also propose a concept of conjugate points rules which are suitable for special registering applications, and compare it to three typical conjugate points rules. Finally, we explain how this method can be used for the co-registration of real 3D point sets and show coregistration results based on airborne laser scanner data. Concluding results of our experiment suggest that the proposed method has a good performance of 3D surface matching, and the least normal distance rule returns the best result for the strip adjustment of airborne laser altimetry data.
Vehicle detection in dense urban areas is often complicated due to car-like objects on rooftops which result in false positive detections. This can be easily avoided by using a digital surface model (DSM) calculated from two consecutive images to exclude those regions. However, in the real-time case traffic information has to be gathered rapidly and the calculation of the DSM for the whole image takes a lot of time. The presented approach suggest a method where the disparity image is only calculated for areas of interest. These areas are selected by projecting the road segments from a road database in the original image using the collinearity equation. The local coordinates of the detected vehicles are then transformed back in the UTM coordinate system using the collinearity equation again. It can be shown that the search area for the detector is significantly reduced and which also leads to improved results of the detection.
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