Abstract
In order to speed up the image processing for self-driving cars, we propose a solution for fast vehicle classification using GPU computation. Our solution uses Histogram of Oriented Gradients (HOG) for feature extraction and Support Vector Machines (SVM) for classification. Our algorithm achieves a higher processing rate in frames per second (FPS) by using multi-core GPUs without compromising on its accuracy. The implementation of our GPU programming is in OpenCL, which is a platform independent library. We used a dataset of images of cars and other non-car objects on road to feed it to the classifier.
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Acknowledgments
This project was undertaken for the final year course in B. Tech. Computer Engineering at Veermata Jijabai Technological Institute, Mumbai for the academic year 2017–2018. It was made possible with the guidance of Mrs. Varshapriya J N, Assistant Professor, Dept. of Computer Engineering.
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Prabhu, S., Khopkar, V., Nivendkar, S., Satpute, O., Jyotinagar, V. (2020). Object Detection and Classification Using GPU Acceleration. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_18
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DOI: https://doi.org/10.1007/978-3-030-37218-7_18
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