TY - CHAP
T1 - Weighted histogram of oriented uniform gradients for moving object detection
AU - Yang, Wei Jong
AU - Su, Yu Xiang
AU - Chung, Pau Choo
AU - Yang, Jar Ferr
N1 - Funding Information:
Acknowledgements. This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 105-2221-E-006-065-MY3.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - With the growth of the automotive electronics technology, the advanced driver assistance system (ADAS) becomes more and more important. Especially, the moving object detection (MOD) is an important issue in the ADAS in intelligent vehicles. In realistic systems, there exist two critical challenges including computing time and detection rate for MOD. To overcome these problems, we propose a novel moving object detection system which contains pre-processing, feature extraction, classification and state machine. The pre-processing contains ROI extraction and skipping low busyness windows, which accelerates the computing time to solve the mentioned problem. To improve the performances, in this paper, the weighted histogram of oriented uniform gradient (WHOUG) with support vector machine (SVM) is proposed to promote the detection accuracy. Besides, the finite state machine could further improve the robustness of the proposed system. The results demonstrate that the proposed system achieves better performance than the traditional one, and also maintains real time computation.
AB - With the growth of the automotive electronics technology, the advanced driver assistance system (ADAS) becomes more and more important. Especially, the moving object detection (MOD) is an important issue in the ADAS in intelligent vehicles. In realistic systems, there exist two critical challenges including computing time and detection rate for MOD. To overcome these problems, we propose a novel moving object detection system which contains pre-processing, feature extraction, classification and state machine. The pre-processing contains ROI extraction and skipping low busyness windows, which accelerates the computing time to solve the mentioned problem. To improve the performances, in this paper, the weighted histogram of oriented uniform gradient (WHOUG) with support vector machine (SVM) is proposed to promote the detection accuracy. Besides, the finite state machine could further improve the robustness of the proposed system. The results demonstrate that the proposed system achieves better performance than the traditional one, and also maintains real time computation.
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U2 - 10.1007/978-3-030-12388-8_18
DO - 10.1007/978-3-030-12388-8_18
M3 - Chapter
AN - SCOPUS:85062920759
T3 - Lecture Notes in Networks and Systems
SP - 250
EP - 260
BT - Lecture Notes in Networks and Systems
PB - Springer
ER -