@article{fc9afd21107d44fd9f67a662b63f8b9f,
title = "Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera",
abstract = "This paper presents a robust method for generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS). The highlight of our method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). Our results show an improvement of 90% more true positives per frame compared to one of the state-of-the-art methods. Our proposed method is robust to variations in illumination and to a wide variety of vehicles and obstacles that are encountered while driving. Distortions are introduced when Inverse Perspective Mapping (IPM) projects the camera image onto the road surface plane. In this paper, we first show that the angular distortion in the IPM domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. We use this information to perform proposal generation. We propose a novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction. We also present an annotated obstacle detection dataset derived from various source videos that can serve as a benchmark for the evaluation of future obstacle detection algorithms. The source videos containing diverse illumination and traffic conditions are derived from multiple publicly available datasets.",
keywords = "Advanced Driver Assistance Systems (ADAS), Fused statistical properties, Inverse Perspective Mapping (IPM), Obstacle detection, Occlusion, Proposal assessment, Selective Edge Filtering (SEF)",
author = "Prakash, {Charan D.} and Farshad Akhbari and Lina Karam",
note = "Funding Information: The authors would like to thank Amol Borkar and Mahdi Rezaei for providing access to their datasets. We would also like to thank Intel Corporation for their support of this work. Charan D. Prakash received the B.E. degree in electronics and communication engineering from Visvesvaraya Technological University, Karnataka, India, in 2010, and the M.S. degree in electrical engineering from Arizona State University, Tempe, AZ, USA, in 2012. He is currently in pursuit of his Ph.D. degree in electrical engineering in Arizona State University. His current research interests include interest point detectors, driver assistance systems and structure from motion. He is also a Teaching Assistant in the School of Electrical, Computer and Energy Engineering. He is a member of the Image, Video, and Usability Laboratory with Arizona State University. Farshad Akhbari is a senior software architect with Intel Corporation with 26 years of hands on experience in various aspects of design, development and deployment of vision and data analytics products. Currently, he is responsible for system architecture path finding and performance optimization for autonomous driving systems. He has contributed to more than 50 invention disclosures and eleven publications in the field of perception enabling, artificial intelligence and autonomous driving. Lina J. Karam , Fellow, IEEE, received the B.E. degree in computer and communications engineering from the American University of Beirut, Beirut, Lebanon, in 1989 and the M.S. and Ph.D. degrees in electrical engineering from the Georgia Institute of Technology, Atlanta, GA, USA, in 1992 and 1995, respectively. She is the Computer Engineering Director for Industry Engagement, and a Full Professor in the School of Electrical, Computer & Energy Engineering, Arizona State University, where she also directs the Image, Video, & Usabilty (IVU) Research Laboratory. Her industrial experience includes image and video compression development at AT&T Bell Labs, Murray Hill, NJ, USA, multidimensional data processing and visualization at Schlumberger, and collaboration on computer vision, machine learning, image/video processing, compression, and transmission projects with industries including Intel, Google, Qualcomm, NTT, Motorola, General Dynamics, and NASA. She has over 200 technical publications and she is an inventor on a number of issued patents. Dr. Karam was awarded a U.S. National Science Foundation CAREER Award, a NASA Technical Innovation Award, 2018 IEEE Region 6 Award, 2014 IEEE SPS Best Paper Award, 2012 Intel Outstanding Researcher Award, and 2012 IEEE Phoenix Section Outstanding Faculty Award. Dr. Karam is the Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing. She served on the IEEE PSPB Strategic Planning Committee, IEEE SPS Board of Governors, IEEE CAS Fellow Evaluation Committee, and on the editorial boards of several journals. She served as the General Chair of the 2016 IEEE ICIP, Technical Program Chair of the 2009 IEEE ICIP, General Chair of the 2011 IEEE DSP/SPE Workshops. She cofounded the international QoMEX conference. Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",
year = "2019",
month = apr,
doi = "10.1016/j.robot.2018.12.004",
language = "English (US)",
volume = "114",
pages = "172--186",
journal = "Robotics and Autonomous Systems",
issn = "0921-8890",
publisher = "Elsevier",
}