Abnormal Motion Areas Detection for Advanced Driver Assistance System

DOI

10.2991/meees-18.2018.73How to use a DOI?

Keywords

abnormal motion; abnormality; MMM; AMAD-ADAS; Reinhard.

Abstract

This paper proposes a novel abnormal motion detection method for advanced driver assistance system (AMAD-ADAS) containing techniques of lane markings detection, motion areas detection and abnormality quantification model. First, lane markings are detected based on Hough transform. Later, a novel motion areas detection method consisting of multiple layers operation, multiple motions operation and multiple areas operation (termed MMM method) is approved to detect candidate motion areas, which is more suitable for real environment than the traditional appearance-based method that can only detect specified object, such as vehicles and pedestrians. Finally, an abnormality quantification model is estimated to quantify the abnormality of each candidate area obtained by MMM method. The AMAD-ADAS shows its robustness through many experiments on the Reinhard [1] datasets.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianping Chen
AU  - Husheng Liao
AU  - Lihua Fu
PY  - 2018/05
DA  - 2018/05
TI  - Abnormal Motion Areas Detection for Advanced Driver Assistance System
BT  - Proceedings of the 2018 International Conference on Mechanical, Electrical, Electronic Engineering & Science (MEEES 2018)
PB  - Atlantis Press
SP  - 415
EP  - 419
SN  - 2352-5401
UR  - https://doi.org/10.2991/meees-18.2018.73
DO  - 10.2991/meees-18.2018.73
ID  - Chen2018/05
ER  -