GPU Implementations of Object Detection using HOG Features and Deformable Models

Manato Hirabayashi, Shinpei Kato, Masato Edahiro, Kazuya Takeda, Taiki Kawano, Seiichi Mita
School of Information Science, Nagoya University
1st IEEE International Conference on Cyber-Physical Systems, Networks, and Applications (CPSNA’13), 2013


   title={GPU Implementations of Object Detection using HOG Features and Deformable Models},

   author={Hirabayashi, Manato and Kato, Shinpei and Edahiro, Masato and Takeda, Kazuya and Kawano, Taiki and Mita, Seiichi},



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Vision-based object detection using camera sensors is an essential piece of perception for autonomous vehicles. Various combinations of features and models can be applied to increase the quality and the speed of object detection. A well-known approach uses histograms of oriented gradients (HOG) with deformable models to detect a car in an image [15]. A major challenge of this approach can be found in computational cost introducing a real-time constraint relevant to the real world. In this paper, we present an implementation technique using graphics processing units (GPUs) to accelerate computations of scoring similarity of the input image and the pre-defined models. Our implementation considers the entire program structure as well as the specific algorithm for practical use. We apply the presented technique to the real-world vehicle detection program and demonstrate that our implementation using commodity GPUs can achieve speedups of 3x to 5x in frame-rate over sequential and multithreaded implementations using traditional CPUs.
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