{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:24:51Z","timestamp":1772724291099,"version":"3.50.1"},"reference-count":159,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSERC-SPG"},{"name":"NSERC-CREATE TRANSIT"},{"name":"NSERC-DISCOVERY"},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs Program","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>Intelligent transportation systems (ITS) enable transportation participants to communicate with each other by sending and receiving messages, so that they can be aware of their surroundings and facilitate efficient transportation through better decision making. As an important part of ITS, autonomous vehicles can bring massive benefits by reducing traffic accidents. Correspondingly, much effort has been paid to the task of pedestrian detection, which is a fundamental task for supporting autonomous vehicles. With the progress of computational power in recent years, adopting deep learning\u2013based methods has become a trend for improving the performance of pedestrian detection. In this article, we present design guidelines on deep learning\u2013based pedestrian detection methods for supporting autonomous vehicles. First, we will introduce classic backbone models and frameworks, and we will analyze the inherent attributes of pedestrian detection. Then, we will illustrate and analyze representative pedestrian detectors from occlusion handling, multi-scale feature extraction, multi-perspective data utilization, and hard negatives handling these four aspects. Last, we will discuss the developments and trends in this area, followed by some open challenges.<\/jats:p>","DOI":"10.1145\/3460770","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T04:13:04Z","timestamp":1628050384000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Design Guidelines on Deep Learning\u2013based Pedestrian Detection Methods for Supporting Autonomous Vehicles"],"prefix":"10.1145","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3851-9938","authenticated-orcid":false,"given":"Azzedine","family":"Boukerche","sequence":"first","affiliation":[{"name":"University of Ottawa, Ottawa, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-5603","authenticated-orcid":false,"given":"Mingzhi","family":"Sha","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, ON, Canada"}]}],"member":"320","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. In SAE MOBILUS. Retrieved from https:\/\/www.sae.org\/standards\/content\/j3016_201806\/.  2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. In SAE MOBILUS. Retrieved from https:\/\/www.sae.org\/standards\/content\/j3016_201806\/."},{"key":"e_1_2_1_2_1","volume-title":"6 Key Connectivity Requirements of Autonomous Driving","unstructured":"2020. 6 Key Connectivity Requirements of Autonomous Driving . In IEEE Spectrum. Retrieved from https:\/\/spectrum.ieee.org\/transportation\/advanced-cars\/6-key-connectivity-requirements-of-autonomous-driving. 2020. 6 Key Connectivity Requirements of Autonomous Driving. In IEEE Spectrum. Retrieved from https:\/\/spectrum.ieee.org\/transportation\/advanced-cars\/6-key-connectivity-requirements-of-autonomous-driving."},{"key":"e_1_2_1_3_1","volume-title":"New Level 3 Autonomous Vehicles Hitting the Road","year":"2020","unstructured":"2020. New Level 3 Autonomous Vehicles Hitting the Road in 2020 . In IEEE Innovation at Work. Retrieved from https:\/\/innovationatwork.ieee.org\/new-level-3-autonomous-vehicles-hitting-the-road-in-2020\/. 2020. New Level 3 Autonomous Vehicles Hitting the Road in 2020. In IEEE Innovation at Work. Retrieved from https:\/\/innovationatwork.ieee.org\/new-level-3-autonomous-vehicles-hitting-the-road-in-2020\/."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2006.244"},{"key":"e_1_2_1_5_1","volume-title":"Proc. IEEE GIIS. 9\u201315","author":"Alemneh E.","unstructured":"E. Alemneh , S. Senouci , and P. Brunet . 2017. PV-Alert: A fog-based architecture for safeguarding vulnerable road users . In Proc. IEEE GIIS. 9\u201315 . E. Alemneh, S. Senouci, and P. Brunet. 2017. PV-Alert: A fog-based architecture for safeguarding vulnerable road users. In Proc. IEEE GIIS. 9\u201315."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.32"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00177"},{"key":"e_1_2_1_8_1","volume-title":"Proc. IEEE DAC. 1\u20136.","author":"Baidya S.","unstructured":"S. Baidya , Y. J. Ku , H. Zhao , J. Zhao , and S. Dey . 2020. Vehicular and edge computing for emerging connected and autonomous vehicle applications . In Proc. IEEE DAC. 1\u20136. S. Baidya, Y. J. Ku, H. Zhao, J. Zhao, and S. Dey. 2020. Vehicular and edge computing for emerging connected and autonomous vehicle applications. In Proc. IEEE DAC. 1\u20136."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.993558"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248017"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2020.101248"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2897684"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.530"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.384"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102678"},{"key":"e_1_2_1_17_1","volume-title":"Proc. ECML PKDD. 52\u201368","author":"Chen Shang-Tse","year":"2018","unstructured":"Shang-Tse Chen , Cory Cornelius , Jason Martin , and Duen Horng Polo Chau . 2018 . Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector . In Proc. ECML PKDD. 52\u201368 . Shang-Tse Chen, Cory Cornelius, Jason Martin, and Duen Horng Polo Chau. 2018. Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector. In Proc. ECML PKDD. 52\u201368."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765695"},{"key":"e_1_2_1_19_1","unstructured":"Siew-Kei Lam Chengju Zhou and Meiqing Wu. 2019. SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1902.09080.  Siew-Kei Lam Chengju Zhou and Meiqing Wu. 2019. SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1902.09080."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6690"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_1_22_1","volume-title":"Proc. IEEE CVPR. 886\u2013893","author":"Dalal N.","unstructured":"N. Dalal and B. Triggs . 2005. Histograms of oriented gradients for human detection . In Proc. IEEE CVPR. 886\u2013893 . N. Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proc. IEEE CVPR. 886\u2013893."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.259"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2300479"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33709-3_46"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.24.68"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.23.91"},{"key":"e_1_2_1_29_1","volume-title":"Proc. IEEE CVPR. 304\u2013311","author":"Dollar P.","unstructured":"P. Dollar , C. Wojek , B. Schiele , and P. Perona . 2009. Pedestrian detection: A benchmark . In Proc. IEEE CVPR. 304\u2013311 . P. Dollar, C. Wojek, B. Schiele, and P. Perona. 2009. Pedestrian detection: A benchmark. In Proc. IEEE CVPR. 304\u2013311."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"key":"e_1_2_1_31_1","unstructured":"Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. 2019. Data Augmentation Using GANs. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1904.09135.  Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. 2019. Data Augmentation Using GANs. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1904.09135."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"e_1_2_1_33_1","volume-title":"Proc. IEEE ICCV. 1\u20138.","author":"Ess A.","unstructured":"A. Ess , B. Leibe , and L. Van Gool . 2007. Depth and appearance for mobile scene analysis . In Proc. IEEE ICCV. 1\u20138. A. Ess, B. Leibe, and L. Van Gool. 2007. Depth and appearance for mobile scene analysis. In Proc. IEEE ICCV. 1\u20138."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.167"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_1_37_1","volume-title":"Proc. IEEE ISBI. 289\u2013293","author":"Frid-Adar M.","unstructured":"M. Frid-Adar , E. Klang , M. Amitai , J. Goldberger , and H. Greenspan . 2018. Synthetic data augmentation using GAN for improved liver lesion classification . In Proc. IEEE ISBI. 289\u2013293 . M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan. 2018. Synthetic data augmentation using GAN for improved liver lesion classification. In Proc. IEEE ISBI. 289\u2013293."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2016.2599783"},{"key":"e_1_2_1_39_1","volume-title":"Proc. IEEE CVPR. 3354\u20133361","author":"Geiger A.","unstructured":"A. Geiger , P. Lenz , and R. Urtasun . 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite . In Proc. IEEE CVPR. 3354\u20133361 . A. Geiger, P. Lenz, and R. Urtasun. 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proc. IEEE CVPR. 3354\u20133361."},{"key":"e_1_2_1_40_1","volume-title":"Fast R-CNN. In Proc. IEEE ICCV. 1440\u20131448","author":"Girshick Ross","year":"2015","unstructured":"Ross Girshick . 2015 . Fast R-CNN. In Proc. IEEE ICCV. 1440\u20131448 . Ross Girshick. 2015. Fast R-CNN. In Proc. IEEE ICCV. 1440\u20131448."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1207\/STHF0203_1"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2639450"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106717"},{"key":"e_1_2_1_45_1","volume-title":"Proc. IEEE ICPR. 621\u2013626","author":"Choi Hangil","unstructured":"Hangil Choi , S. Kim , Kihong Park , and K. Sohn . 2016. Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks . In Proc. IEEE ICPR. 621\u2013626 . Hangil Choi, S. Kim, Kihong Park, and K. Sohn. 2016. Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks. In Proc. IEEE ICPR. 621\u2013626."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"e_1_2_1_47_1","volume-title":"Saad Ullah Akram, and Ling Shao","author":"Hasan Irtiza","year":"2020","unstructured":"Irtiza Hasan , Shengcai Liao , Jinpeng Li , Saad Ullah Akram, and Ling Shao . 2020 . Pedestrian Detection : The Elephant In The Room. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/2003.08799. Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, and Ling Shao. 2020. Pedestrian Detection: The Elephant In The Room. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/2003.08799."},{"key":"e_1_2_1_48_1","volume-title":"Proc. IEEE WCNC. 1\u20136.","author":"Hbaieb A.","unstructured":"A. Hbaieb , J. Rezgui , and L. Chaari . 2019. Pedestrian Detection for autonomous driving within cooperative communication system . In Proc. IEEE WCNC. 1\u20136. A. Hbaieb, J. Rezgui, and L. Chaari. 2019. Pedestrian Detection for autonomous driving within cooperative communication system. In Proc. IEEE WCNC. 1\u20136."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.4271\/2020-01-0091"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299034"},{"key":"e_1_2_1_54_1","unstructured":"Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1704.04861.  Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1704.04861."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_1_56_1","volume-title":"Proc. NIPS. 1635\u20131646","author":"Hu Shengyuan","unstructured":"Shengyuan Hu , Tao Yu , Chuan Guo , Wei-Lun Chao , and Kilian Q. Weinberger . 2019. A new defense against adversarial images: Turning a weakness into a strength . In Proc. NIPS. 1635\u20131646 . Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, and Kilian Q. Weinberger. 2019. A new defense against adversarial images: Turning a weakness into a strength. In Proc. NIPS. 1635\u20131646."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00821"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00083"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"e_1_2_1_60_1","unstructured":"Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;1MB model size. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1602.07360.  Forrest N. Iandola Matthew W. Moskewicz Khalid Ashraf Song Han William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;1MB model size. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1602.07360."},{"key":"e_1_2_1_61_1","volume-title":"Proc. ICML. 448\u2013456","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch normalization: Accelerating deep network training by reducing internal covariate shift . In Proc. ICML. 448\u2013456 . Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. ICML. 448\u2013456."},{"key":"e_1_2_1_62_1","volume-title":"Proc. NIPS. 2017\u20132025","author":"Jaderberg Max","year":"2015","unstructured":"Max Jaderberg , Karen Simonyan , Andrew Zisserman , et\u00a0al. 2015 . Spatial transformer networks . In Proc. NIPS. 2017\u20132025 . Max Jaderberg, Karen Simonyan, Andrew Zisserman, et\u00a0al. 2015. Spatial transformer networks. In Proc. NIPS. 2017\u20132025."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301962"},{"key":"e_1_2_1_64_1","volume-title":"Proc. IEEE AICCSA. 358\u2013362","author":"Jegham I.","unstructured":"I. Jegham and A. Ben Khalifa . 2017. Pedestrian detection in poor weather conditions using moving camera . In Proc. IEEE AICCSA. 358\u2013362 . I. Jegham and A. Ben Khalifa. 2017. Pedestrian detection in poor weather conditions using moving camera. In Proc. IEEE AICCSA. 358\u2013362."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.042"},{"key":"e_1_2_1_66_1","volume-title":"Proc. BMVC. 73","author":"Jingjing Liu Shu Wang","year":"2016","unstructured":"Shu Wang Jingjing Liu , Shaoting Zhang and Dimitris Metaxas . 2016 . Multispectral deep neural networks for pedestrian detection . In Proc. BMVC. 73 .1\u201373.13. Shu Wang Jingjing Liu, Shaoting Zhang and Dimitris Metaxas. 2016. Multispectral deep neural networks for pedestrian detection. In Proc. BMVC. 73.1\u201373.13."},{"key":"e_1_2_1_67_1","volume-title":"Board: Enabling autonomous vehicles with embedded systems. In Proc","author":"Kato S.","year":"2018","unstructured":"S. Kato , S. Tokunaga , Y. Maruyama , S. Maeda , M. Hirabayashi , Y. Kitsukawa , A. Monrroy , T. Ando , Y. Fujii , and T. Azumi . 2018 . Autoware on Board: Enabling autonomous vehicles with embedded systems. In Proc . IEEE ICCPS. S. Kato, S. Tokunaga, Y. Maruyama, S. Maeda, M. Hirabayashi, Y. Kitsukawa, A. Monrroy, T. Ando, Y. Fujii, and T. Azumi. 2018. Autoware on Board: Enabling autonomous vehicles with embedded systems. In Proc. IEEE ICCPS."},{"key":"e_1_2_1_68_1","unstructured":"Kaveh Bakhsh Kelarestaghi Mahsa Foruhandeh Kevin Heaslip and Ryan Gerdes. 2019. Survey on vehicular ad hoc networks and its access technologies security vulnerabilities and countermeasures. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1903.01541.  Kaveh Bakhsh Kelarestaghi Mahsa Foruhandeh Kevin Heaslip and Ryan Gerdes. 2019. Survey on vehicular ad hoc networks and its access technologies security vulnerabilities and countermeasures. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1903.01541."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.07.020"},{"key":"e_1_2_1_70_1","volume-title":"Proc. NIPS. 1097\u20131105","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E. Hinton . 2012. Imagenet classification with deep convolutional neural networks . In Proc. NIPS. 1097\u20131105 . Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proc. NIPS. 1097\u20131105."},{"key":"e_1_2_1_71_1","volume-title":"Proc. IEEE ICIP. 4207\u20134211","author":"Kruthiventi S. S. S.","unstructured":"S. S. S. Kruthiventi , P. Sahay , and R. Biswal . 2017. Low-light pedestrian detection from RGB images using multi-modal knowledge distillation . In Proc. IEEE ICIP. 4207\u20134211 . S. S. S. Kruthiventi, P. Sahay, and R. Biswal. 2017. Low-light pedestrian detection from RGB images using multi-modal knowledge distillation. In Proc. IEEE ICIP. 4207\u20134211."},{"key":"e_1_2_1_72_1","volume-title":"Proc. IEEE ITSC. 1695\u20131701","author":"Kutila M.","unstructured":"M. Kutila , P. Pyykonen , H. Holzhuter , M. Colomb , and P. Duthon . 2018. Automotive LiDAR performance verification in fog and rain . In Proc. IEEE ITSC. 1695\u20131701 . M. Kutila, P. Pyykonen, H. Holzhuter, M. Colomb, and P. Duthon. 2018. Automotive LiDAR performance verification in fog and rain. In Proc. IEEE ITSC. 1695\u20131701."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_75_1","doi-asserted-by":"crossref","unstructured":"J. Levinson J. Askeland J. Becker J. Dolson D. Held S. Kammel J. Z. Kolter D. Langer O. Pink V. Pratt M. Sokolsky G. Stanek D. Stavens A. Teichman M. Werling and S. Thrun. 2011. Towards fully autonomous driving: Systems and algorithms. In IEEE IV. 163\u2013168.  J. Levinson J. Askeland J. Becker J. Dolson D. Held S. Kammel J. Z. Kolter D. Langer O. Pink V. Pratt M. Sokolsky G. Stanek D. Stavens A. Teichman M. Werling and S. Thrun. 2011. Towards fully autonomous driving: Systems and algorithms. In IEEE IV. 163\u2013168.","DOI":"10.1109\/IVS.2011.5940562"},{"key":"e_1_2_1_76_1","volume-title":"Proc. NIPS. 9464\u20139474","author":"Li Bai","year":"2019","unstructured":"Bai Li , Changyou Chen , Wenlin Wang , and Lawrence Carin . 2019 . Certified adversarial robustness with additive noise . In Proc. NIPS. 9464\u20139474 . Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin. 2019. Certified adversarial robustness with additive noise. In Proc. NIPS. 9464\u20139474."},{"key":"e_1_2_1_77_1","unstructured":"G. Li Y. Yang and X. Qu. 2019. Deep learning approaches on pedestrian detection in hazy weather. IEEE Trans. Ind. Electron. (2019) 1\u20131. Early Access.  G. Li Y. Yang and X. Qu. 2019. Deep learning approaches on pedestrian detection in hazy weather. IEEE Trans. Ind. Electron. (2019) 1\u20131. Early Access."},{"key":"e_1_2_1_78_1","first-page":"985","article-title":"Scale-aware fast R-CNN for pedestrian detection","volume":"20","author":"Li Jianan","year":"2017","unstructured":"Jianan Li , Xiaodan Liang , ShengMei Shen , Tingfa Xu , Jiashi Feng , and Shuicheng Yan . 2017 . Scale-aware fast R-CNN for pedestrian detection . IEEE Trans. Multimedia 20 , 4 (2017), 985 \u2013 996 . Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, Jiashi Feng, and Shuicheng Yan. 2017. Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20, 4 (2017), 985\u2013996.","journal-title":"IEEE Trans. Multimedia"},{"key":"e_1_2_1_79_1","doi-asserted-by":"crossref","unstructured":"Junwei Liang Lu Jiang and Alexander Hauptmann. 2020. SimAug: Learning Robust Representations from Simulation for Trajectory Prediction. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/2004.02022.  Junwei Liang Lu Jiang and Alexander Hauptmann. 2020. SimAug: Learning Robust Representations from Simulation for Trajectory Prediction. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/2004.02022.","DOI":"10.1007\/978-3-030-58601-0_17"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_45"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3296957.3173191"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_2_1_84_1","volume-title":"Proc. ECCV. 740\u2013755","author":"Lin Tsung-Yi","unstructured":"Tsung-Yi Lin , Michael Maire , Serge Belongie , James Hays , Pietro Perona , Deva Ramanan , Piotr Doll\u00e1r , and C. Lawrence Zitnick . 2014. Microsoft COCO: Common objects in context . In Proc. ECCV. 740\u2013755 . Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proc. ECCV. 740\u2013755."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00662"},{"key":"e_1_2_1_86_1","volume-title":"Berg","author":"Liu Wei","year":"2016","unstructured":"Wei Liu , Dragomir Anguelov , Dumitru Erhan , Christian Szegedy , Scott Reed , Cheng-Yang Fu , and Alexander C . Berg . 2016 . SSD : Single shot multibox detector. In Proc. ECCV. 21\u201337. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. 2016. SSD: Single shot multibox detector. In Proc. ECCV. 21\u201337."},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00533"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.120"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-016-1258-3"},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3416014.3424600"},{"key":"e_1_2_1_92_1","volume-title":"Proc. IEEE ICVES. 1\u20136.","author":"Maurya S. K.","unstructured":"S. K. Maurya and A. Choudhary . 2018. Deep learning\u2013based vulnerable road user detection and collision avoidance . In Proc. IEEE ICVES. 1\u20136. S. K. Maurya and A. Choudhary. 2018. Deep learning\u2013based vulnerable road user detection and collision avoidance. In Proc. IEEE ICVES. 1\u20136."},{"key":"e_1_2_1_93_1","volume-title":"C","author":"Minematsu Tsubasa","year":"2017","unstructured":"Tsubasa Minematsu , Hideaki Uchiyama , Atsushi Shimada , Hajime Nagahara , and Rin-ichiro Taniguchi. 2017. Adaptive background model registration for moving cameras. Pattern Recogn. Lett. 96 , C ( 2017 ), 86\u201395. Tsubasa Minematsu, Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, and Rin-ichiro Taniguchi. 2017. Adaptive background model registration for moving cameras. Pattern Recogn. Lett. 96, C (2017), 86\u201395."},{"key":"e_1_2_1_94_1","volume-title":"Proc. ICML. 807\u2013814","author":"Nair Vinod","unstructured":"Vinod Nair and Geoffrey E. Hinton . 2010. Rectified linear units improve restricted boltzmann machines . In Proc. ICML. 807\u2013814 . Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proc. ICML. 807\u2013814."},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00107"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00107"},{"key":"e_1_2_1_98_1","unstructured":"Nvidia. [n.d.]. NVIDIA DRIVE AGX PEGASUS. [Online]. Retrieved from https:\/\/www.nvidia.com\/en-us\/self-driving-cars\/drive-platform\/hardware\/.  Nvidia. [n.d.]. NVIDIA DRIVE AGX PEGASUS. [Online]. Retrieved from https:\/\/www.nvidia.com\/en-us\/self-driving-cars\/drive-platform\/hardware\/."},{"key":"e_1_2_1_99_1","unstructured":"Nvidia. [n.d.]. NVIDIA DRIVE SIM AND DRIVE CONSTELLATION. [Online]. Retrieved from https:\/\/www.nvidia.com\/en-us\/self-driving-cars\/drive-constellation\/.  Nvidia. [n.d.]. NVIDIA DRIVE SIM AND DRIVE CONSTELLATION. [Online]. Retrieved from https:\/\/www.nvidia.com\/en-us\/self-driving-cars\/drive-constellation\/."},{"key":"e_1_2_1_100_1","volume-title":"Proc. IEEE CVPR. 3258\u20133265","author":"Ouyang W.","unstructured":"W. Ouyang and X. Wang . 2012. A discriminative deep model for pedestrian detection with occlusion handling . In Proc. IEEE CVPR. 3258\u20133265 . W. Ouyang and X. Wang. 2012. A discriminative deep model for pedestrian detection with occlusion handling. In Proc. IEEE CVPR. 3258\u20133265."},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.257"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.411"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.414"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2738645"},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_36"},{"key":"e_1_2_1_106_1","volume-title":"Proc. IEEE IV. 1768\u20131774","author":"Palffy A.","unstructured":"A. Palffy , J. F. P. Kooij , and D. M. Gavrila . 2019. Occlusion aware sensor fusion for early crossing pedestrian detection . In Proc. IEEE IV. 1768\u20131774 . A. Palffy, J. F. P. Kooij, and D. M. Gavrila. 2019. Occlusion aware sensor fusion for early crossing pedestrian detection. In Proc. IEEE IV. 1768\u20131774."},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00507"},{"key":"e_1_2_1_108_1","volume-title":"Proc. IEEE ICCV. 555\u2013562","author":"Papageorgiou C. P.","unstructured":"C. P. Papageorgiou , M. Oren , and T. Poggio . 1998. A general framework for object detection . In Proc. IEEE ICCV. 555\u2013562 . C. P. Papageorgiou, M. Oren, and T. Poggio. 1998. A general framework for object detection. In Proc. IEEE ICCV. 555\u2013562."},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1998.710772"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.371"},{"key":"e_1_2_1_111_1","volume-title":"Proc. IEEE IV. 14\u201319","author":"Pereira Joel","unstructured":"Joel Pereira , Cristiano Premebida , Alireza Asvadi , F. Cannata , Luis Garrote , and U. J. Nunes . 2019. Test and evaluation of connected and autonomous vehicles in real-world scenarios . In Proc. IEEE IV. 14\u201319 . Joel Pereira, Cristiano Premebida, Alireza Asvadi, F. Cannata, Luis Garrote, and U. J. Nunes. 2019. Test and evaluation of connected and autonomous vehicles in real-world scenarios. In Proc. IEEE IV. 14\u201319."},{"key":"e_1_2_1_112_1","volume-title":"Proc. IEEE CVPR. 6517\u20136525","author":"Redmon J.","unstructured":"J. Redmon and A. Farhadi . 2017. YOLO9000: Better, faster, stronger . In Proc. IEEE CVPR. 6517\u20136525 . J. Redmon and A. Farhadi. 2017. YOLO9000: Better, faster, stronger. In Proc. IEEE CVPR. 6517\u20136525."},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2019.12.012"},{"key":"e_1_2_1_114_1","volume-title":"Proc. NIPS. 91\u201399","author":"Ren Shaoqing","year":"2015","unstructured":"Shaoqing Ren , Kaiming He , Ross Girshick , and Jian Sun . 2015 . Faster R-CNN: Towards real-time object detection with region proposal networks . In Proc. NIPS. 91\u201399 . Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Proc. NIPS. 91\u201399."},{"key":"e_1_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"e_1_2_1_116_1","volume-title":"Proc. IEEE ISCC. 618\u2013623","author":"Sha M.","unstructured":"M. Sha and A. Boukerche . 2020. Semantic fusion-based pedestrian detection for supporting autonomous vehicles . In Proc. IEEE ISCC. 618\u2013623 . M. Sha and A. Boukerche. 2020. Semantic fusion-based pedestrian detection for supporting autonomous vehicles. In Proc. IEEE ISCC. 618\u2013623."},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vlsi.2017.07.007"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"},{"key":"e_1_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1145\/3416013.3426461"},{"key":"e_1_2_1_120_1","volume-title":"Proc. ICLR.","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman . 2015 . Very deep convolutional networks for large-scale image recognition. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1409.1556. In Proc. ICLR. Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. [Online]. Retrieved from http:\/\/arxiv.org\/abs\/1409.1556. In Proc. ICLR."},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_33"},{"key":"e_1_2_1_122_1","unstructured":"Jack Stewart. [n.d.]. Self-Driving Cars Use Crazy Amounts of Power and It\u2019s Becoming a Problem. [Online]. Retrieved from https:\/\/www.wired.com\/story\/self-driving-cars-power-consumption-nvidia-chip\/.  Jack Stewart. [n.d.]. Self-Driving Cars Use Crazy Amounts of Power and It\u2019s Becoming a Problem. [Online]. Retrieved from https:\/\/www.wired.com\/story\/self-driving-cars-power-consumption-nvidia-chip\/."},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2018.2878109"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0664-6"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.221"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299143"},{"key":"e_1_2_1_127_1","volume-title":"Proc. BMVC. 175","author":"Toca Cosmin","year":"2015","unstructured":"Cosmin Toca , Mihai Ciuc , and Carmen Patrascu . 2015 . Normalized autobinomial Markov channels for pedestrian detection . In Proc. BMVC. 175 .1\u2013175.13. Cosmin Toca, Mihai Ciuc, and Carmen Patrascu. 2015. Normalized autobinomial Markov channels for pedestrian detection. In Proc. BMVC. 175.1\u2013175.13."},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.214"},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0620-5"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.02.012"},{"key":"e_1_2_1_131_1","volume-title":"Proc. IEOM. 178\u2013191","author":"Varghese Jaycil Z.","year":"2015","unstructured":"Jaycil Z. Varghese , Randy G. Boone , et\u00a0al. 2015 . Overview of autonomous vehicle sensors and systems . In Proc. IEOM. 178\u2013191 . Jaycil Z. Varghese, Randy G. Boone, et\u00a0al. 2015. Overview of autonomous vehicle sensors and systems. In Proc. IEOM. 178\u2013191."},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000013087.49260.fb"},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.331"},{"key":"e_1_2_1_134_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2016.11.014"},{"key":"e_1_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2829602"},{"key":"e_1_2_1_136_1","volume-title":"Lau","author":"Wang Tianyu","year":"2019","unstructured":"Tianyu Wang , Xin Yang , Ke Xu , Shaozhe Chen , Qiang Zhang , and Rynson W. H . Lau . 2019 . Spatial attentive single-image deraining with a high quality real rain dataset. In Proc. IEEE CVPR. Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W. H. Lau. 2019. Spatial attentive single-image deraining with a high quality real rain dataset. In Proc. IEEE CVPR."},{"key":"e_1_2_1_137_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.324"},{"key":"e_1_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00811"},{"key":"e_1_2_1_140_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-69321-5_9"},{"key":"e_1_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_1_142_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.153"},{"key":"e_1_2_1_143_1","volume-title":"Proc. IEEE IV. 171\u2013178","author":"Xiong H.","unstructured":"H. Xiong , F. B. Flohr , S. Wang , B. Wang , J. Wang , and K. Li . 2019. Recurrent neural network architectures for vulnerable road user trajectory prediction . In Proc. IEEE IV. 171\u2013178 . H. Xiong, F. B. Flohr, S. Wang, B. Wang, J. Wang, and K. Li. 2019. Recurrent neural network architectures for vulnerable road user trajectory prediction. In Proc. IEEE IV. 171\u2013178."},{"key":"e_1_2_1_144_1","volume-title":"Proc. IEEE ICCV. 82\u201390","author":"Yang Bin","unstructured":"Bin Yang , Junjie Yan , Zhen Lei , and Stan Z. Li . 2015. Convolutional channel features . In Proc. IEEE ICCV. 82\u201390 . Bin Yang, Junjie Yan, Zhen Lei, and Stan Z. Li. 2015. Convolutional channel features. In Proc. IEEE ICCV. 82\u201390."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2019.2892497"},{"key":"e_1_2_1_146_1","volume-title":"Patel","author":"Zhang He","year":"2018","unstructured":"He Zhang and Vishal M . Patel . 2018 . Densely connected pyramid dehazing network. In Proc. IEEE CVPR. He Zhang and Vishal M. Patel. 2018. Densely connected pyramid dehazing network. In Proc. IEEE CVPR."},{"key":"e_1_2_1_147_1","volume-title":"CSID","author":"Zhang Jialiang","year":"2019","unstructured":"Jialiang Zhang , Lixiang Lin , Yun-chen Chen, Yao Hu , Steven C. H. Hoi , and Jianke Zhu . 2019 . CSID : Center , Scale, Identity and Density-aware Pedestrian Detectionin a Crowd. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/1910.09188. Jialiang Zhang, Lixiang Lin, Yun-chen Chen, Yao Hu, Steven C. H. Hoi, and Jianke Zhu. 2019. CSID: Center, Scale, Identity and Density-aware Pedestrian Detectionin a Crowd. [Online]. Retrieved from https:\/\/arxiv.org\/abs\/1910.09188."},{"key":"e_1_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_28"},{"key":"e_1_2_1_149_1","volume-title":"Cremers","author":"Zhang Shanshan","year":"2014","unstructured":"Shanshan Zhang , Christian Bauckhage , and Armin B . Cremers . 2014 . Informed haar-like features improve pedestrian detection. In Proc. IEEE CVPR. Shanshan Zhang, Christian Bauckhage, and Armin B. Cremers. 2014. Informed haar-like features improve pedestrian detection. In Proc. IEEE CVPR."},{"key":"e_1_2_1_150_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.141"},{"key":"e_1_2_1_151_1","volume-title":"Proc. IEEE CVPR. 4457\u20134465","author":"Zhang S.","unstructured":"S. Zhang , R. Benenson , and B. Schiele . 2017. CityPersons: A diverse dataset for pedestrian detection . In Proc. IEEE CVPR. 4457\u20134465 . S. Zhang, R. Benenson, and B. Schiele. 2017. CityPersons: A diverse dataset for pedestrian detection. In Proc. IEEE CVPR. 4457\u20134465."},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298784"},{"key":"e_1_2_1_153_1","volume-title":"Li","author":"Zhang Shifeng","year":"2018","unstructured":"Shifeng Zhang , Longyin Wen , Xiao Bian , Zhen Lei , and Stan Z . Li . 2018 . Occlusion-aware R-CNN: Detecting pedestrians in a crowd. In Proc. ECCV. 637\u2013653. Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, and Stan Z. Li. 2018. Occlusion-aware R-CNN: Detecting pedestrians in a crowd. In Proc. ECCV. 637\u2013653."},{"key":"e_1_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00731"},{"key":"e_1_2_1_155_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2818018"},{"key":"e_1_2_1_156_1","volume-title":"Proc. IEEE Trans. Intell. Transp. Syst.765\u2013770","author":"Jia Zhen","unstructured":"Zhen Jia , A. Balasuriya , and S. Challa . 2006. Recent developments in vision based target tracking for autonomous vehicles navigation . In Proc. IEEE Trans. Intell. Transp. Syst.765\u2013770 . Zhen Jia, A. Balasuriya, and S. Challa. 2006. Recent developments in vision based target tracking for autonomous vehicles navigation. In Proc. IEEE Trans. Intell. Transp. Syst.765\u2013770."},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.377"},{"key":"e_1_2_1_158_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_9"},{"key":"e_1_2_1_159_1","volume-title":"Proc. ACCV. 416\u2013430","author":"Zhu Yousong","year":"2016","unstructured":"Yousong Zhu , Jinqiao Wang , Chaoyang Zhao , Haiyun Guo , and Hanqing Lu . 2016 . Scale-adaptive deconvolutional regression network for pedestrian detection . In Proc. ACCV. 416\u2013430 . Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo, and Hanqing Lu. 2016. Scale-adaptive deconvolutional regression network for pedestrian detection. In Proc. ACCV. 416\u2013430."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:28Z","timestamp":1750195468000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,3]]},"references-count":159,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,7,31]]}},"alternative-id":["10.1145\/3460770"],"URL":"https:\/\/doi.org\/10.1145\/3460770","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,3]]},"assertion":[{"value":"2020-03-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}