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Cited by 81Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation

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Zhang, Fukai, Li, Ce, Yang, Feng·2019·Sensors
DOI 10.3390/s19030594PMC PMC6387095
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Abstract

Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 × 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 × 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.

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  1. [1]Chu W. Liu Y. Shen C. Cai D. Hua X.S. Multi-Task Vehicle Detection With Region-of-Interest Voting IEEE Trans. Image Process. 2018 27 432 441 10.1109/TIP.2017.2762591 29028197 · DOI
  2. [3]Loukas G. Vuong T. Heartfield R. Sakellari G. Yoon Y. Gan D. Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning IEEE Access 2018 6 3491 3508 10.1109/ACCESS.2017.2782159 · DOI
  3. [4]Sivaraman S. Trivedi M.M. A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking IEEE Trans. Intell. Transp. Syst. 2010 11 267 276 10.1109/TITS.2010.2040177 · DOI
  4. [5]Niknejad H.T. Takeuchi A. Mita S. McAllester D. On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation IEEE Trans. Intell. Transp. Syst. 2012 13 748 758 10.1109/TITS.2012.2187894 · DOI
  5. [6]Girshick R. Donahue J. Darrell T. Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Columbus, OH, USA 23–28 June 2014 580 587
  6. [7]He K. Zhang X. Ren S. Sun J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition arXiv 2015 1406.4729v4 10.1109/TPAMI.2015.2389824 26353135 · DOI · Paper page
  7. [8]Girshick R. Fast R-CNN Proceedings of the IEEE International Conference on Computer Vision (ICCV) Santiago, Chile 7–13 December 2015 1440 1448
  8. [10]Uijlings J.R.R. Sande K.E.A. Gevers T. Smeulders A.W.M. Selective Search for Object Recognition Int. J. Comput. Vis. 2013 104 154 171 10.1007/s11263-013-0620-5 · DOI · Paper page
  9. [12]Liu W. Anguelov D. Erhan D. Szegedy C. Reed S. Fu C.Y. Berg A.C. SSD: Single Shot MultiBox Detector Proceedings of the Computer Vision—ECCV Amsterdam, The Netherlands 11–14 October 2016 21 37
  10. [13]Jeong J. Park H. Kwak N. Enhancement of SSD by concatenating feature maps for object detection arXiv 2017 1705.09587v1
  11. [14]Ghodrati A. Diba A. Pedersoli M. Tuytelaars T. Gool L.V. DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers Proceedings of the IEEE International Conference on Computer Vision Santiago, Chile 7–13 December 2015 2578 2586
  12. [16]Szegedy C. Liu W. Jia Y. Sermanet P. Reed S. Anguelov D. Erhan D. Vanhoucke V. Rabinovich A. Going Deeper with Convolutions arXiv 2014 1409.4842v1
  13. [17]Simonyan K. Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition Proceedings of the Neural Information Processing Systems (NIPS) Montréal, QC, Canada 7–12 December 2015
  14. [18]He K. Zhang X. Ren S. Sun J. Deep Residual Learning for Image Recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016
  15. [20]Szegedy C. Reed S. Erhan D. Anguelov D. Ioffe S. Scalable, High-Quality Object Detection arXiv 2015 1412.1441v3
  16. [21]Bell S. Zitnick L. Bala K. Girshick R. Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016
  17. [22]Liu W. Rabinovich A. Berg A.C. ParseNet: Looking Wider to See Better arXiv 2015 1506.04579
  18. [23]Kong T. Yao A. Chen Y. Sun F. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016
  19. [24]Redmon J. Farhadi A. YOLO9000: Better, Faster, Stronger arXiv 2016 1612.08242v1
  20. [25]Fu C.Y. Liu W. Ranga A. Tyagi A. Berg A.C. DSSD: Deconvolutional Single Shot Detector arXiv 2017 1701.06659v1
  21. [26]Zhang S. Wen L. Bian X. Lei Z. Li S.Z. Single-Shot Refinement Neural Network for Object Detection arXiv 2018 1711.06897
  22. [27]Liu Y. Wang R. Shan S. Chen X. Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Salt Lake City, UT, USA 18–22 June 2018
  23. [28]Zhou P. Scale-Transferrable Object Detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Salt Lake City, UT, USA 18–22 June 2018
  24. [29]Redmon J. Farhadi A. YOLOv3: An Incremental Improvement arXiv 2018 1804.02767v1
  25. [30]Huang S.C. Do B.H. Radial basis function based neural network for motion detection in dynamic scenes IEEE Trans. Cybern. 2014 44 114 125 10.1109/TCYB.2013.2248057 24108721 · DOI
  26. [31]Candès E.J. Li X. Ma Y. Wright J. Robust principal component analysis J. ACM 2011 58 1 37 10.1145/1970392.1970395 · DOI
  27. [32]Xu H. Caramanis C. Sanghavi S. Robust PCA via outlier pursuit IEEE Trans. Inf. Theory 2012 58 3047 3064 10.1109/TIT.2011.2173156 · DOI
  28. [33]Chen B.H. Huang S.C. Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems Inf. Sci. 2015 299 283 295 10.1016/j.ins.2014.12.033 · DOI
  29. [34]Wang L. Lu Y. Wang H. Zheng Y. Ye H. Xue X. Evolving Boxes for Fast Vehicle Detection arXiv 2017 1702.00254v3
  30. [35]Cai Y. Wang H. Zheng Z. Sun X. Scene-Adaptive Vehicle Detection Algorithm Based on a Composite Deep Structure IEEE Access 2017 5 22804 22811 10.1109/ACCESS.2017.2756081 · DOI
  31. [36]Kuang H. Zhang X. Li Y.J. Yan H. Nighttime Vehicle Detection Based on Bio-Inspired Image Enhancement and Weighted Score-Level Feature Fusion IEEE Trans. Intell. Transp. Syst. 2017 18 927 936 10.1109/TITS.2016.2598192 · DOI
  32. [37]Ji X. Zhang G. Chen X. Guo Q. Multi-Perspective Tracking for Intelligent Vehicle IEEE Trans. Intell. Transp. Syst. 2018 19 518 529 10.1109/TITS.2017.2784486 · DOI
  33. [38]Zhou Y. Liu L. Shao L. Mellor M. DAVE: A Unied Framework for Fast Vehicle Detection and Annotation arXiv 2016 1607.04564v3
  34. [39]Yang L. Luo P. Loy C.C. Tang X. A Large-Scale Car Dataset for Fine-Grained Categorization and Verification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA 7–12 June 2015
  35. [40]Everingham M. Gool L.V. Williams C.K.I. Winn J. Zisserman A. The Pascal Visual Object Classes (VOC) Challenge Int. J. Comput. Vis. 2010 88 303 338 10.1007/s11263-009-0275-4 · DOI · Paper page
  36. [41]Tang T. Zhou S. Deng Z. Lei L. Zou H. Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks Remote Sens. 2017 9 1170 10.3390/rs9111170 PMC5335960 28208587 · DOI
  37. [42]Tang T. Zhou S. Deng Z. Zou H. Lei L. Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining Sensors 2017 17 336 10.3390/s17020336 PMC5335960 28208587 · DOI
  38. [43]Deng Z. Sun H. Zhou S. Zhao J. Zou H. Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017 10 3652 3664 10.1109/JSTARS.2017.2694890 · DOI · Paper page
  39. [44]Qu T. Zhang Q. Sun S. Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks Multimedia Tools Appl. 2017 76 21651 21663 10.1007/s11042-016-4043-5 · DOI
  40. [45]Ioffe S. Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift arXiv 2015 1502.03167v3
  41. [46]Shelhamer E. Long J. Darrell T. Fully Convolutional Networks for Semantic Segmentation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA 7–12 June 2015
  42. [48]Lecun Y. Boser B. Denker J.S. Henderson D. Howard R.E. Hubbard W. Backpropagation Applied to Handwritten Zip Code Recognition Neural Comput. 1989 1 541 551 10.1162/neco.1989.1.4.541 · DOI · Paper page
  43. [49]Glorot X. Bengio Y. Understanding the difficulty of training deep feedforward neural networks Proceedings of the thirteenth international conference on artificial intelligence and statistics Sardinia, Italy 13–15 May 2010 249 256
  44. [50]Lyu S. Chang M.C. Du D. Wen L. Qi H. Li Y. Wei Y. Ke L. Hu T. Coco M.D. UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Lecce, Italy 29 August–1 September 2017
  45. [51]Wen L. Du D. Cai Z. Lei Z. Chang M.C. Qi H. Lim J. Yang M.H. Lyu S. UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking arXiv 2015 1511.04136v3
  46. [52]Geiger A. Philip G. Urtasun R. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Providence, RI, USA 16–21 June 2012
  47. [53]Jia Y. Shelhamer E. Donahue J. Karayev S. Long J. Girshick R. Guadarrama S. Darrell T. Caffe: Convolutional Architecture for Fast Feature Embedding arXiv 2014 1408.5093v1
  48. [54]Fredrik G. Erik L.N. Automotive 3D Object Detection Without Target Domain Annotations Master’s Thesis Linköping University Linköping, Sweden 2018
  49. [55]Yang F. Choi W. Lin Y. Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016
  50. [56]Chen X. Ma H. Wan J. Li B. Xia T. Multi-View 3D Object Detection Network for Autonomous Driving Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu, HI, USA 21–26 July 2017
  51. [57]Rajarm R. Bar E. Trivedi M. RefineNet: Refining Object Detectors for Autonomous Driving IEEE Trans. Intell. Veh. 2016 1 358 368 10.1109/TIV.2017.2695896 · DOI
  52. [58]Rajarm R. Bar E. Trivedi M. RefineNet: Iterative Refinement for Accurate Object Localization Proceedings of the Intelligent Transportation Systems Conference Rio de Janeiro, Brazil 1–4 November 2016
  53. [59]Hu Q. Paisitkriangkrai S. Shen C. Hengel A. Porikli F. Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework IEEE Trans. Intell. Transp. Syst. 2016 17 1002 1014 10.1109/TITS.2015.2496795 · DOI
  54. [60]Stewart R. Andriluka M. Ng A. End-to-End People Detection in Crowded Scenes Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016

Cited by

Sorted by citations

Authors
Zhang, F., Li, C., & Yang, F.
Affiliations
  • China University of Mining and Technology - Beijing (CN)
  • School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, Beijing 100083, China
Corresponding author
Zhang, Fukai
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2019-01-30
Volume
19
Issue
3
Pages
594
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s19030594
PMC
PMC6387095
OpenAlex
W2911466075
Cited by
81
References
6
Citations by year
2026: 2 · 2025: 10 · 2024: 10 · 2023: 11 · 2022: 17 · 2021: 14 · 2020: 13 · 2019: 4
FWCI
5.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (61601466)
  • State Key Laboratory of Coal Resources and Safe Mining (SKLCRSM16KFD04)
  • Fundamental Research Funds for the Central Universities (2016QJ04)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
vehicle detection, urban traffic surveillance, Single Shot MultiBox Detector, feature pyramid, deconvolution, pooling, feature concatenation, small object localization, real-time object detection, UA-DETRAC, KITTI

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