Abstract
In this work, a statistical analysis of object detection for stereo vision-based driver assistance systems is presented. Analytic modelling has not been attempted previously due to the complexity of dense disparity maps and state-of-the-art algorithms. To approach this problem, a simplified algorithm for object detection in stereo images which allows studying error propagation is considered. In order to model the input densities, vehicle contours are approximated by Gaussian Mixture Models and distance dependent measurement noise is taken into account. Theoretical results are verified with Monte Carlo methods and real-world image sequences. Using the proposed model, a prediction on the uncertainty in object location and optimal threshold selection can be obtained.
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Stellet, J.E., Schumacher, J., Lange, O., Branz, W., Niewels, F., Zöllner, J.M. (2016). Statistical Modelling of Object Detection in Stereo Vision-Based Driver Assistance. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_54
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DOI: https://doi.org/10.1007/978-3-319-08338-4_54
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