Koba Natroshvili

Fast and reliable recognition of supplementary traffic signs

Supplementary traffic signs are used to alter the meaning of other traffic signs. Assistance systems that recognize traffic signs therefore must also recognize supplementary signs to evaluate their influence on the meaning of detected traffic signs. We propose an algorithm which is able to detect supplementary signs in the vicinity of other signs using a novel rectangle segmentation algorithm. Support vector machines are used for the classification and rejection of other objects. The combination of both components permits to recognize a supplementary sign in less than 40 ms. First quantitative results for a test set with four different supplementary sign types show a very good classification accuracy of more than 96 %.

Real time pedestrian detection by fusing PMD and CMOS cameras

In this work we present the preliminary results of the fusion of photonic mixer device - PMD and CMOS cameras for driver assistance applications. Although the algorithms are demonstrated mainly for pedestrians, they apply to the other objects on the street. PMD camera delivers the 3D object list. Object coordinates are further projected into CMOS image plane where classification is performed using support vector machines. As compared to PMD camera the CMOS camera has higher resolution, which gives the possibility to realize finer object detection, separation and classification. As the feature vector we use quadruple haar discrete wavelet transformation (QH DWT). The speed improvement of the SVM in the testing phase (necessary for real-time implementation) is realized with Burgpsilas reduced set vector method (BRSVM), improving classification speed nearly 70 times. We have achieved the pedestrian detection rate of 80%.

Automatic extrinsic calibration methods for Surround View Systems

A typical Surround View System consists of several cameras on the vehicle perimeter. This document proposes three novel methods for the extrinsic calibration of Surround View Systems (SVS). I — The first approach uses a single calibration pattern placed step-by-step on the vehicle perimeter. II — The second approach uses several calibration patterns placed on the ground plane. The vehicle drives between the calibration setup. No knowledge of the vehicles location relative to the calibration patterns is necessary; only distances between the calibration patterns are required. III — The last approach gives the most flexibility in calibration. The features are distributed on the ground plane arbitrarily. The vehicle drives forward and backward and all extrinsic calibration parameters are automatically estimated. Each of these approaches provide very good calibration results, sufficient for the correct operation of SVS.