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A multi-target corner pooling-based neural network for vehicle detection

  • Extreme Learning Machine and Deep Learning Networks
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Abstract

Convolutional neural network has shown strong capability to improve performance in vehicle detection, which is one of the main research topics of intelligent transportation system. Aiming to detect the blocked vehicles efficiently in actual traffic scenes, we propose a novel convolutional neural network based on multi-target corner pooling layers. The hourglass network, which could extract local and global information of the vehicles in the images simultaneously, is chosen as the backbone network to provide vehicles’ features. Instead of using the max pooling layer, the proposed multi-target corner pooling (MTCP) layer is used to generate the vehicles’ corners. And in order to complete the blocked corners that cannot be generated by MTCP, a novel matching corners method is adopted in the network. Therefore, the proposed network can detect blocked vehicles accurately. Experiments demonstrate that the proposed network achieves an AP of 43.5% on MS COCO dataset and a precision of 93.6% on traffic videos, which outperforms the several existing detectors.

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References

  1. Yang H, Qu S (2017) Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition. IET Intell Transp Syst 12(1):75–85

    Article  MathSciNet  Google Scholar 

  2. Jo Y, Jung I (2014) Analysis of vehicle detection with WSN-based ultrasonic sensors. Sensors 14(8):14050–14069

    Article  Google Scholar 

  3. Kim DH, Choi KH, Li KJ, Lee YS (2015) Performance of vehicle speed estimation using wireless sensor networks: a region-based approach. J Supercomput 71(6):2102–2120

    Google Scholar 

  4. Bin T, Brendan TM, Shao T et al (2014) Hierarchical and networked vehicle surveillance in its: a survey. IEEE Trans Intell Transp Syst 16(2):1–24

    Google Scholar 

  5. Unzueta L, Nieto M, Cortes A, Barandiaran J, Otaegui O, Sanchez P (2012) Adaptive multicue background subtraction for robust vehicle counting and classification. IEEE Trans Intell Transp Syst 13(2):527–540

    Article  Google Scholar 

  6. Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100

    Article  Google Scholar 

  7. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 580–587

  8. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 779–788

  9. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision (ECCV), pp 21–37

  10. Chen X, Xiang S, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801

    Article  Google Scholar 

  11. Gao Y, Guo S, Huang K, Gong Q, Zou Y, Bai T, Overett G, Chen J (2017) Scale optimization for full-image-CNN vehicle detection. In: IEEE intelligent vehicles symposium (IV), pp 785–791

  12. Ren S, He K, Girshick R, Zhang X, Sun J (2015) Object detection networks on convolutional feature maps. IEEE Trans Pattern Anal Mach Intell 39(7):1476–1481

    Article  Google Scholar 

  13. Tian B, Tang M, Wang FY (2015) Vehicle detection grammars with partial occlusion handling for traffic surveillance. Transp Res Part C Emerging Technol 56:80–93

    Article  Google Scholar 

  14. Naushad Ali MM, Abdullah-Al-Wadud M, Lee SL (2014) Multiple object tracking with partial occlusion handling using salient feature points. Inf Sci 278:448–465

    Article  Google Scholar 

  15. Velazquez-Pupo R, Sierra-Romero A, Torres-Roman D, Shkvarko YV, Santiago-Paz J, Gómez-Gutiérrez D, Robles-Valdez D, Hermosillo-Reynoso F, Romero-Delgado M (2018) Vehicle detection with occlusion handling, tracking, and OC-SVM classification: a high performance vision-based system. Sensors (Basel) 18(2):374

    Article  Google Scholar 

  16. Girshick R (2015) Fast rcnn. In: IEEE international conference on computer vision (ICCV), pp 1440–1448

  17. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  18. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 6517–6525

  19. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

  20. Fu CY, Liu W, Ranga A, Tyagi A, Berg AC (2017) DSSD: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659

  21. Newell A, Yang K, Deng J (2016) Stacked hourglass networks for human pose estimation, In: European conference on computer vision (ECCV), pp 483–499

  22. Lin TY, Goyal P, Grishck R, He K, Dollar P (2017) Focal loss for dense object detection. In: IEEE international conference on computer vision (ICCV), pp 2999–3007

  23. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 580–587

  24. Newell A, Huang Z, Deng J (2017) Associative embedding: end-to-end learning for joint detection and grouping. In: Advances in neural information processing systems, pp 2274–2284

  25. Li H, Gao G, Chen R, Ge X, Guo S, Hao LY (2019) The Influence Ranking for Testers in Bug Tracking Systems. Int J Softw Eng Knowl Eng 29(01):93–113

    Article  Google Scholar 

  26. Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision (ECCV), pp 740–755

  27. Paszke A, Gross S, Chintala S, Chanan G, Yang E, Devito Z, Lin Z, Desmaison A, Antiga L and Leter A (2017) Automatic differentiation in PyTorch. In: Proceedings of the workshop on autodiff decision program, pp 1–4

  28. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  29. Guo M (2012) The Fudan University traffic video and data sharing platform. http://traflow.fudan.edu.cn. Accessed 28 Mar 2012

  30. Tychsen-Smith L, Petersson L (2017) Denet: scalable real-time object detection with directed sparse sampling. In: IEEE international conference on computer vision (ICCV), pp 428–436

  31. Zhu Y, Zhao C, Wang J, Zhao X, Wu Y, Lu H (2017) Couplenet: coupling global structure with local parts for object detection. In: IEEE international conference on computer vision (ICCV), pp 4126–4134

  32. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: IEEE international conference on computer vision (ICCV), pp 764–773

  33. Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-nms improving object detection with one line of code. In: IEEE international conference on computer vision (ICCV), pp 5562–5570

  34. Shen Z, Liu Z, Li J, Jiang YG, Chen Y, Xue X (2017) DSOD: Learning deeply supervised object detectors from scratch. In: IEEE international conference on computer vision (ICCV), pp 1937–1945

  35. D Zhang S, Wen L, Bian X, Lei Z, Li SZ (2017) Single-shot refinement neural network for object detection. arXiv preprint arXiv:1711.06897

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61503055, 61573077, U1808205, 61602077), Liaoning Natural Science Foundation (2019-KR-03-09), Dalian Innovative support scheme for high-level talents (2017RQ072) and the Fundamental Research Funds for the Central University (3132019104).

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  1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, People’s Republic of China

    Li-Ying Hao, Jie Li & Ge Guo

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  1. Li-Ying Hao
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  2. Jie Li
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  3. Ge Guo
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Correspondence to Ge Guo.

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Hao, LY., Li, J. & Guo, G. A multi-target corner pooling-based neural network for vehicle detection. Neural Comput & Applic 32, 14497–14506 (2020). https://doi.org/10.1007/s00521-019-04486-1

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