Gpu architecture for stationary multisensor pedestrian detection at smart intersections

Daniel Weimer, Sebastian Kohler, Christian Hellert, Konrad Doll, Ulrich Brunsmann, Roland Krzikalla
University of Applied Sciences Aschaffenburg, Germany
IEEE Intelligent Vehicles Symposium (IV), 2011


   title={Gpu architecture for stationary multisensor pedestrian detection at smart intersections},

   author={Weimer, D. and Kohler, S. and Hellert, C. and Doll, K. and Brunsmann, U. and Krzikalla, R.},

   booktitle={Intelligent Vehicles Symposium (IV), 2011 IEEE},





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We present a real-time multisensor architecture for combined laser scanner and infra-red video-based pedestrian detection and tracking used within a road side unit for intersection assistance. In order to achieve outmost classification performance we propose a cascaded classifier using laser scanner hypothesis generation and histogram of oriented gradients (HOG) descriptors for video-based classification together with linear and Gaussian kernel support vector machines (SVM). The entire classification cascade is implemented on a graphics processing unit (GPU). Giving real-time performance top priority, we present novel compute unified device architecture (CUDA) implementations of a selective HOG-based feature extraction and background subtraction based on mixture of Gaussians (MOG). The classification cascade is managed by a multi-core CPU that further performs pedestrian tracking using a linear Kalman filter. Evaluation on an infra-red benchmark database and an experimental study on a real-world intersection used within the Ko-PER project confirm excellent classification and real-time performance around the clock without external illumination.
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