期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：7
DOI：10.14569/IJACSA.2017.080740
出版社：Science and Information Society (SAI)
摘要：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%.
关键词：ADAS; customized HOG; linear SVM; obstacle detection
