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Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Sarfraz Ahmed, M. Nazmul Huda, Sujan Rajbhandari, Chitta Saha, Mark Elshaw, Stratis Kanarachos
<span title="2019-06-06">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector  ...  This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users.  ...  Acknowledgments: We would like to acknowledge the support of James Spooner from Centre for Connected and Autonomous Automotive Research, Coventry University.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9112335">doi:10.3390/app9112335</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/55zsz77zcjblpg2lmutnubfpdm">fatcat:55zsz77zcjblpg2lmutnubfpdm</a> </span>
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Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs? [article]

Eduardo Romera, Luis M. Bergasa, Roberto Arroyo
<span title="2016-07-04">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Autonomous driving is a challenging topic that requires complex solutions in perception tasks such as recognition of road, lanes, traffic signs or lights, vehicles and pedestrians.  ...  However, the recent appearance of Convolutional Neural Networks (CNNs) has revolutionized the computer vision field and has made possible approaches to perform full pixel-wise semantic segmentation in  ...  CONCLUSION AND FUTURE WORK This work supposes a prior study to design a perception approach for autonomous vehicles fully based in segmentation performed by CNNs, which will be reviewed in future works  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.00971v1">arXiv:1607.00971v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pxw6lqmy2nfizgme4cxfjrt3my">fatcat:pxw6lqmy2nfizgme4cxfjrt3my</a> </span>
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Generalizable Pedestrian Detection: The Elephant In The Room [article]

Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, Ling Shao
<span title="2020-12-09">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Pedestrian detection is used in many vision based applications ranging from video surveillance to autonomous driving.  ...  Through this study, we find that existing state-of-the-art pedestrian detectors, though perform quite well when trained and tested on the same dataset, generalize poorly in cross dataset evaluation.  ...  Some of the pioneer works for CNN based pedestrian detection [19, 46] used R-CNN framework [15] , which is still the most popular framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.08799v7">arXiv:2003.08799v7</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o3x5rdsvq5binh6ky4ymooymqm">fatcat:o3x5rdsvq5binh6ky4ymooymqm</a> </span>
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Hand-Crafted Features vs Deep Learning for Pedestrian Detection in Moving Camera

Bilel Tarchoun, Anouar Khalifa, Selma Dhifallah, Imen Jegham, Mohamed Mahjou
<span title="2020-04-30">2020</span> <i title="International Information and Engineering Technology Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wsuaubw4tnbbhjln4ji2em2dmi" style="color: black;">Traitement du signal</a> </i> &nbsp;
We also compare both systems' performances to other state-of-the-art pedestrian detectors.  ...  In this paper, we propose two pedestrian detectors for use in images taken from a moving vehicle: The first detector uses a block matching algorithm and handcraft features for pedestrian detection, and  ...  detector designed for use with a camera installed on a vehicle. • We compare the performance of our detector to other handcraft feature based detectors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/ts.370206">doi:10.18280/ts.370206</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gqkrz6z7tzfatmpfsbhrlkv34a">fatcat:gqkrz6z7tzfatmpfsbhrlkv34a</a> </span>
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Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, Challenges

Hrag-Harout Jebamikyous, Rasha Kashef
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  ...  The Viola-Jones algorithm was used to create nine object detectors classified under four groups: traffic light detector, pedestrian detector, traffic sign detector, and vehicle detector.  ...  The authors in [13] proposed an encoder-decoder-based deep CNN model in semantic segmentation of autonomous vehicle scenarios. The proposed model architecture is based on the VGG16 model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3144407">doi:10.1109/access.2022.3144407</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/27zpuomnxzbs3gl3ab55a46wru">fatcat:27zpuomnxzbs3gl3ab55a46wru</a> </span>
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2nd Place Solution for Waymo Open Dataset Challenge – 2D Object Detection [article]

Sijia Chen, Yu Wang, Li Huang, Runzhou Ge, Yihan Hu, Zhuangzhuang Ding, Jie Liao
<span title="2020-06-28">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios.  ...  A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons.  ...  The positive IoU thresholds are set to 0.7, 0.5, and 0.5 for evaluating vehicles, cyclists, and pedestrians, respectively. Implementation Details Cascade R-CNN Detector.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.15507v1">arXiv:2006.15507v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkvnlo4y7bhjxhdshmqlkwuh3m">fatcat:vkvnlo4y7bhjxhdshmqlkwuh3m</a> </span>
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Visual and Thermal Data for Pedestrian and Cyclist Detection [chapter]

Sarfraz Ahmed, M. Nazmul Huda, Sujan Rajbhandari, Chitta Saha, Mark Elshaw, Stratis Kanarachos
<span title="">2019</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety.  ...  This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction.  ...  [24] For the fusion models, a CNN-based was implemented for comparison of the performance of fused data and single data approaches (see Fig. 3 ).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-25332-5_20">doi:10.1007/978-3-030-25332-5_20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xmckbzuxkvf3vlsgoeuw7l3xk4">fatcat:xmckbzuxkvf3vlsgoeuw7l3xk4</a> </span>
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Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance

Manuel Carranza-García, Pedro Lara-Benítez, Jorge García-Gutiérrez, José C. Riquelme
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bby322qx6ndsje4ypr56c7nnly" style="color: black;">Neurocomputing</a> </i> &nbsp;
This study presents an enhanced 2D object detector based on Faster R-CNN that is better suited for the context of autonomous vehicles.  ...  Therefore, we propose a perspective-aware methodology that divides the image into key regions via clustering and uses evolutionary algorithms to optimize the base anchors for each of them.  ...  Acknowledgments We are grateful to NVIDIA for their GPU Grant Program that has provided us the high-quality GPU devices for carrying out the study.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neucom.2021.04.001">doi:10.1016/j.neucom.2021.04.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kx3jqph5uffyrc2jmap2tlot5u">fatcat:kx3jqph5uffyrc2jmap2tlot5u</a> </span>
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RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance

Jongwon Kim, Jeongho Cho
<span title="2021-03-01">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Consequently, it was shown that the proposed method has a superior performance for the detection of vehicles and pedestrians than conventional vision-based methods.  ...  Performance tests and evaluation of the proposed RGDiNet were conducted under various operating conditions using hand-labeled aerial datasets.  ...  Acknowledgments: The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21051677">doi:10.3390/s21051677</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33804364">pmid:33804364</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qehpjmlb4vam5okh2ycof7pw2q">fatcat:qehpjmlb4vam5okh2ycof7pw2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210306073700/https://res.mdpi.com/d_attachment/sensors/sensors-21-01677/article_deploy/sensors-21-01677-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/d7/85d75f9336c261917303a5d0c02f6659f9703fb3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21051677"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Information feedback loop for improved pedestrian detection in an autonomous perception system

Martin Dimitrievski, Peter Veelaert, Wilfried Philips
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cfmch5qrm5ckxpkho4uhbkgznm" style="color: black;">2018 21st International Conference on Intelligent Transportation Systems (ITSC)</a> </i> &nbsp;
In this paper we propose a novel detector-tracker feedback loop for information exchange based on the spatio-temporal similarity of detections and tracklets.  ...  Environmental perception systems for autonomous vehicles are often built using heterogeneous technologies that operate in a sequential manner.  ...  More recently, the proliferation of high performance GPU computing paved the way for Convolutional Neural Network (CNN) based detectors by utilizing simple, yet efficient deep learning algorithms for training  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/itsc.2018.8569968">doi:10.1109/itsc.2018.8569968</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/itsc/DimitrievskiVP18.html">dblp:conf/itsc/DimitrievskiVP18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7fwgr2wqfndevdulstps3af6ne">fatcat:7fwgr2wqfndevdulstps3af6ne</a> </span>
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When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark

Xiao Wang, Jun Chen, Zheng Wang, Wu Liu, Shin'ichi Satoh, Chao Liang, Chia-Wen Lin
<span title="">2020</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging  ...  Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances.  ...  Acknowledgments Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2020/71">doi:10.24963/ijcai.2020/71</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/WangC0LSLL20.html">dblp:conf/ijcai/WangC0LSLL20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3bds3ezsara5xe22kiv6pazmra">fatcat:3bds3ezsara5xe22kiv6pazmra</a> </span>
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Predicting Pedestrian Intention to Cross The Road

Karam M. AbuGhalieh, Shadi G. Alawneh
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
We built a Convolutional Neural Network (CNN) model combined with depth sensing camera to estimate the pedestrian orientation and distance from the vehicle.  ...  The process of crossing pedestrian is defined as the changing of pedestrian orientation on the curb toward the road.  ...  detector, the detector is based on detecting sudden changes of pedestrian orientation toward the street.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2987777">doi:10.1109/access.2020.2987777</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vnfxy6aknzamvkzhc4ee4n5qee">fatcat:vnfxy6aknzamvkzhc4ee4n5qee</a> </span>
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A scenario generation pipeline for autonomous vehicle simulators

Mingyun Wen, Jisun Park, Kyungeun Cho
<span title="2020-06-03">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7vmvy44msfazpg7esvwjkcglla" style="color: black;">Human-Centric Computing and Information Sciences</a> </i> &nbsp;
Evaluation of CNN-based scenario agent selector To verify the performance of the CNN-based scenario agent selector, we compared the predicted accuracy with three kinds of support vector machines (SVMs)  ...  To ensure that selected agents can perform a given action naturally, we use CNN to evaluate the rationality of the selected agents because CNN has been shown to perform classification tasks well [37]  ...  JP provided action data for generation of scenario. KC provided full guidance. All authors read and approved the final manuscript. Funding Availability of data and materials Not applicable.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13673-020-00231-z">doi:10.1186/s13673-020-00231-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5efrf22g4ba6varbg6z6avu5yu">fatcat:5efrf22g4ba6varbg6z6avu5yu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200604161358/https://hcis-journal.springeropen.com/track/pdf/10.1186/s13673-020-00231-z" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b5/74/b574a337f3b35966bd955d45615e2ad2e4026e16.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s13673-020-00231-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Detection of Pedestrian Actions Based on Deep Learning Approach

D.O. Pop, Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
<span title="2019-12-17">2019</span> <i title="Babes-Bolyai University"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6ives635drbhbgeaommsxl7f3q" style="color: black;">Studia Universitatis Babes-Bolyai: Series Informatica</a> </i> &nbsp;
The pedestrian detection has attracted considerable attention from research due to its vast applicability in the field of autonomous vehicles.  ...  We propose a pedestrian detection component based on Faster R-CNN able to detect the pedestrian and also recognize if the pedestrian is crossing the street in the detecting time.  ...  Train all pedestrian samples also using the pedestrian actions tags (cross/not cross) with the CNN as mentioned above for detection and action recognition based on the Joint Attention for Autonomous Driving  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24193/subbi.2019.2.01">doi:10.24193/subbi.2019.2.01</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6oiew6bjzvdj3nh25qlsg4nsfy">fatcat:6oiew6bjzvdj3nh25qlsg4nsfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191219040808/http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/download/40/40" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/12/37/12374478d36438c28e5b0b3b7cc57c6f049d1752.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24193/subbi.2019.2.01"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Luiz G. Galvao, Maysam Abbod, Tatiana Kalganova, Vasile Palade, Md Nazmul Huda
<span title="2021-10-31">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
However, most of the past papers only reviewed pedestrian or vehicle detection separately.  ...  This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles.  ...  Acknowledgments: We would like to thank Damaris Thompson Dias Borges for helping with the proofreading. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21217267">doi:10.3390/s21217267</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34770575">pmid:34770575</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8587128/">pmcid:PMC8587128</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p7zozlos3jdb7dtbpjdd4fw5ne">fatcat:p7zozlos3jdb7dtbpjdd4fw5ne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220116022620/https://mdpi-res.com/d_attachment/sensors/sensors-21-07267/article_deploy/sensors-21-07267-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/89/44/89447a264f656e1b8ca01070902a0d09b3a228ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21217267"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587128" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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