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DOI: 10.14569/IJACSA.2017.080740
PDF

Customized Descriptor for Various Obstacles Detection in Road Scene

Author 1: Haythem Ameur
Author 2: Abdelhamid Helali
Author 3: J. Ramírez
Author 4: J. M. Gorriz
Author 5: Ridha Mghaieth
Author 6: Hassen Maaref

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 7, 2017.

  • Abstract and Keywords
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Abstract: Recently, real-time object detection systems have become a major challenge in the smart vehicle. In this work, we aim to increase both pedestrian and driver safety through improving their recognition rate in the vehicle’s embedded vision systems. Based on the Histogram of Oriented Gradients (HOG) descriptor, an optimized object detection system is presented in order to achieve an efficient recognition system for several obstacles. The main idea is to customize the weight of each bin in the HOG-feature vector according to its contribution in the description process of the extracted relevant features. Performance studies using a linear SVM classifier prove the efficiency of our approach. Indeed, based on the INRIA datasets, we have improved the sensitivity rate of the pedestrian detection by 11% and the vehicle detection by 5%.

Keywords: ADAS; customized HOG; linear SVM; obstacle detection

Haythem Ameur, Abdelhamid Helali, J. Ramírez, J. M. Gorriz, Ridha Mghaieth and Hassen Maaref, “Customized Descriptor for Various Obstacles Detection in Road Scene” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080740

@article{Ameur2017,
title = {Customized Descriptor for Various Obstacles Detection in Road Scene},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080740},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080740},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {Haythem Ameur and Abdelhamid Helali and J. Ramírez and J. M. Gorriz and Ridha Mghaieth and Hassen Maaref}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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