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Spatial As Deep: Spatial CNN for Traffic Scene Understanding [article]

Xingang Pan, Jianping Shi, Ping Luo, Xiaogang Wang, Xiaoou Tang
<span title="2017-12-17">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall.  ...  In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels  ...  We would like to thank Xiaohang Zhan, Jun Li, and Xudong Cao for helpful work in building the lane detection dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.06080v1">arXiv:1712.06080v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lrtvpp7nnfbmhn2ixc42b5wezi">fatcat:lrtvpp7nnfbmhn2ixc42b5wezi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200829160350/https://arxiv.org/pdf/1712.06080v1.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/28/f5/28f53ec7732299fa946ed3fc27bf691a6ab5c60c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.06080v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Traffic scene recognition based on deep cnn and vlad spatial pyramids [article]

Fang-Yu Wu, Shi-Yang Yan, Jeremy S. Smith, Bai-Ling Zhang
<span title="2017-07-24">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To catch the spatial information, spatial pyramids are exploited to encode CNN features.  ...  The remarkable representational learning capability of CNN remains to be further explored for solving real-world problems.  ...  It will be major achivement to implement an automatic traffic scene recognition system which imitates the human capability to understand traffic scenes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.07411v1">arXiv:1707.07411v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iziuy5zuhvgzvpv5dciikzzlce">fatcat:iziuy5zuhvgzvpv5dciikzzlce</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200928125158/https://arxiv.org/ftp/arxiv/papers/1707/1707.07411.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/4d/89/4d89988b77d4a0da69f4a5bd3a715292f9d57264.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.07411v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Vehicle trajectory prediction in top-view image sequences based on deep learning method [article]

Zahra Salahshoori Nejad, Hamed Heravi, Ali Rahimpour Jounghani, Abdollah Shahrezaie, Afshin Ebrahimi
<span title="2021-05-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
the traffic scene.  ...  Deep learning was used as a tool for extracting the features of these images.  ...  In this section, the model achieves an appropriate understanding of the scene because it has access to the movement status and spatial order of the scene's components.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.01749v3">arXiv:2102.01749v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xta6njyadfeqfhmtfc3jjopoqq">fatcat:xta6njyadfeqfhmtfc3jjopoqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210205052322/https://arxiv.org/ftp/arxiv/papers/2102/2102.01749.pdf" title="fulltext PDF download [not primary version]" 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] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/91/41/9141da70a57cd38015df3eca61f0667248263c2e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.01749v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep Representation Learning and Clustering of Traffic Scenarios [article]

Nick Harmening, Marin Biloš, Stephan Günnemann
<span title="2020-07-15">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions.  ...  Finally, we show how the latent scenario embeddings can be used for clustering traffic scenarios and similarity retrieval.  ...  We are particularly grateful to Tobias Freudling and Marc Neumann for sharing their extensive knowledge in the area of Autonomous Driving and for the productive discussions on the application of the developed  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.07740v1">arXiv:2007.07740v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ozvixjcvsjhqlpd7b2jml5nq3a">fatcat:ozvixjcvsjhqlpd7b2jml5nq3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200729084511/https://arxiv.org/pdf/2007.07740v1.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/1c/98/1c9879676d15d0321f464a716372e81c27a971af.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.07740v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep feature extraction and motion representation for satellite video scene classification

Yanfeng Gu, Huan Liu, Tengfei Wang, Shengyang Li, Guoming Gao
<span title="2020-03-09">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikvx2lmj7rew7jpw4lygqgjpby" style="color: black;">Science China Information Sciences</a> </i> &nbsp;
Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets.  ...  SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events.  ...  Spatial feature extraction based on the deep VGG-Net Representative spatial features are crucial for SVSC.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-019-2784-4">doi:10.1007/s11432-019-2784-4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ozay3xpoxjdpvfg7gnkyehfkrq">fatcat:ozay3xpoxjdpvfg7gnkyehfkrq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210731112246/https://www.sciengine.com/doi/pdf/82FE3BF274C34F038B2E81F48CE95059" 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/f2/74/f2748ab75d0959ab9a7013cab1c01593ff023166.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-019-2784-4"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Deep Learning Methods in Short-Term Traffic Prediction: A Survey

Yue Hou, Xin Zheng, Chengyan Han, Wei Wei, Rafał Scherer, Dawid Połap
<span title="2022-03-26">2022</span> <i title="Kaunas University of Technology (KTU)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7og4indwyvastaeh3sjmrjhj34" style="color: black;">Information Technology and Control</a> </i> &nbsp;
Currently, the most widely used model for short-term traffic prediction are deeplearning models.  ...  used traffic datasets, the mainstream deep learning models and their applications in this field.  ...  For CNN, in short-term traffic prediction, CNN can effectively capture the spatial characteristics of traffic data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5755/j01.itc.51.1.29947">doi:10.5755/j01.itc.51.1.29947</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gp5tra272rdvlmq5nwsas2jqjy">fatcat:gp5tra272rdvlmq5nwsas2jqjy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220327032259/https://itc.ktu.lt/index.php/ITC/article/download/29947/15290" 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/a0/2d/a02df033152136e00577af3c4c27dcb28b5441c2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5755/j01.itc.51.1.29947"> <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>

A comparison of two deep-learning-based urban perception models: which one is better?

Ruifan Wang, Shuliang Ren, Jiaqi Zhang, Yao Yao, Yu Wang, Qingfeng Guan
<span title="2021-03-29">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/73jb6lecrbhgzdrcqcgvthlebi" style="color: black;">Computational Urban Science</a> </i> &nbsp;
In addition, we also find that the CNN-based model is more suitable for scenes with weak spatial heterogeneity (such as small and medium-sized urban environments), while the FCN + RF-based model is applicable  ...  to scenes with strong spatial heterogeneity (such as the downtown areas of China's megacities).  ...  The CNN-based model is more suitable for scenes with weak spatial heterogeneity, such as small cities or suburbs in central China.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s43762-021-00003-0">doi:10.1007/s43762-021-00003-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fpqlyjjaxvd77kpcyy2l5fq2qq">fatcat:fpqlyjjaxvd77kpcyy2l5fq2qq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717200113/https://link.springer.com/content/pdf/10.1007/s43762-021-00003-0.pdf?error=cookies_not_supported&amp;code=2c4a5a84-a407-4102-87ed-cab8b970e5e3" 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/55/98/5598bb02fe59553f9d1ac34869e1c78542e02236.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s43762-021-00003-0"> <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>

Feature Extraction of Broken Glass Cracks in Road Traffic Accident Site Based on Deep Learning

Shuai Liang, Wei Wang
<span title="2021-05-25">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
The image pyramid is constructed and used as the input of the CNN model, and the convolutional layer road traffic accident scene glass breakage and crack characteristics at each scale in the pyramid are  ...  This paper studies the feature extraction and middle-level expression of Convolutional Neural Network (CNN) convolutional layer glass broken and cracked at the scene of road traffic accident.  ...  and cracking of the convolutional road traffic accident scene at each spatial position are separately processed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/5527076">doi:10.1155/2021/5527076</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqijftsofzab5jnou3lrfnnetu">fatcat:jqijftsofzab5jnou3lrfnnetu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210530110825/https://downloads.hindawi.com/journals/complexity/2021/5527076.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/d9/97/d997bf3279a8de8deea290a5ec556d3b8906094c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/5527076"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Deep Rigid Instance Scene Flow [article]

Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, Raquel Urtasun
<span title="2019-04-18">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene  ...  We formulate the problem as energy minimization in a deep structured model, which can be solved efficiently in the GPU by unrolling a Gaussian-Newton solver.  ...  Conclusion In this paper we develop a novel deep structured model for 3D scene flow estimation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.08913v1">arXiv:1904.08913v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hclwv5r3ujef7ihb32m7ppk36i">fatcat:hclwv5r3ujef7ihb32m7ppk36i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191017023210/https://arxiv.org/pdf/1904.08913v1.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/2b/9c/2b9c60dda219b812b4a9227b40706dc920884327.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.08913v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Pedestrian Behavior Understanding and Prediction with Deep Neural Networks [chapter]

Shuai Yi, Hongsheng Li, Xiaogang Wang
<span title="">2016</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;
In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance.  ...  A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN.  ...  Trajectories were most widely used for pedestrian behavior understanding in non-deep-learning approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46448-0_16">doi:10.1007/978-3-319-46448-0_16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k3kk2zbbynfyrnxnrucnmegwwm">fatcat:k3kk2zbbynfyrnxnrucnmegwwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190220082855/http://pdfs.semanticscholar.org/259e/4aca87b8724b4c9df2315b976237481a1929.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/25/9e/259e4aca87b8724b4c9df2315b976237481a1929.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-46448-0_16"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Modeling Required Driver Attention Level Based On Environmental Risk Factors Using Deep Convolutional Neural Networks

Jayani Withanawasam, Ehsan Javanmardi, Yanlei Gu, Shunsuke Kamijo
<span title="">2021</span> <i title="Society of Automotive Engineers of Japan, Inc."> International Journal of Automotive Engineering </i> &nbsp;
We use traffic scenes from Berkley deep drive dataset to evaluate the proposed method.  ...  Understanding the level of environmental risk using vehicle-mounted camera traffic scenes is useful in advanced driver assistance systems (ADAS) to improve vehicle safety.  ...  Further, we clearly demonstrate an Related work Vision based traffic scene understanding Vision-based traffic scene understanding using vehiclemounted cameras has become increasingly popular due to  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20485/jsaeijae.12.4_125">doi:10.20485/jsaeijae.12.4_125</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/373e72hxsvfblbikwj4w2m6xmi">fatcat:373e72hxsvfblbikwj4w2m6xmi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220204023239/https://www.jstage.jst.go.jp/article/jsaeijae/12/4/12_20214935/_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/76/3b/763bbecf9ff70823cc4eeed79540c1b11b00772e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20485/jsaeijae.12.4_125"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review [article]

Abolfazl Razi, Xiwen Chen, Huayu Li, Brendan Russo, Yan Chen, Hongbin Yu
<span title="2022-03-07">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated  ...  We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic  ...  Special thanks go to Greg Leeming from Intel for his insightful comments and continued support of this project. We are grateful to Arizona Commerce  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.10939v1">arXiv:2203.10939v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h4o5zghhhfezncn7luy56yjusm">fatcat:h4o5zghhhfezncn7luy56yjusm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220521082912/https://arxiv.org/pdf/2203.10939v1.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/14/fa/14fab85bb4ef2a8e5e7797103d7a3a2b385aadee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.10939v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Zhiyang Guo, Yingping Huang, Xing Hu, Hongjian Wei, Baigan Zhao
<span title="2021-02-15">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
As a prerequisite for autonomous driving, scene understanding has attracted extensive research.  ...  This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving.  ...  The deep-learning based approaches for scene text detection and recognition for the purpose of scene understanding were reviewed in [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10040471">doi:10.3390/electronics10040471</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyloykg24nbqvlw4ujiiagoneq">fatcat:gyloykg24nbqvlw4ujiiagoneq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429011436/https://res.mdpi.com/d_attachment/electronics/electronics-10-00471/article_deploy/electronics-10-00471.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/f1/6c/f16c7a95b3797dbfd1d8d5b48c424f3d1f862257.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10040471"> <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 Survey of the Recent Architectures of Deep Convolutional Neural Networks [article]

Asifullah Khan, Anabia Sohail, Umme Zahoora, Aqsa Saeed Qureshi
<span title="2020-03-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.  ...  However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations.  ...  Waleed Khan of PIEAS for the detailed discussion related to the Mathematical description of the different CNN architectures.  ... 
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End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies [article]

Hesham M. Eraqi, Mohamed N. Moustafa, Jens Honer
<span title="2017-11-22">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons.  ...  Steering a car through traffic is a complex task that is difficult to cast into algorithms.  ...  A deep CNN learns to extract best driving scene features, and then the resultant sequential feature vectors are passed into a stack of LSTM layers.  ... 
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