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Scale-Aware RPN for Vehicle Detection

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Advances in Visual Computing (ISVC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11241))

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Abstract

In this paper, we develop a scale-aware Region Proposal Network (RPN) model to address the problem of vehicle detection in challenging situations. Our model introduces two built in sub-networks which detect vehicles with scales from disjoint ranges. Therefore, the model is capable of training the specialized sub-networks for large-scale and small-scale vehicles in order to capture their unique characteristics. Meanwhile, high resolution of feature maps for handling small vehicle instances is obtained. The network model is followed by two XGBoost classifiers with bootstrapping strategy for mining hard negative examples. The method is evaluated on the challenging KITTI dataset and achieves comparable results against the state-of-the-art methods.

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References

  1. Ren, S., He, K., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS (2015)

    Google Scholar 

  2. Girshick, R.: Fast R-CNN. In: ICCV (2015)

    Google Scholar 

  3. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: CVPR (2012)

    Google Scholar 

  4. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: ACM SIGKDD, pp. 785–794. ACM (2016)

    Google Scholar 

  5. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR, pp. 73–80 (2010)

    Google Scholar 

  6. Li, J., Liang, X., et al.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20, 985–996 (2017)

    Google Scholar 

  7. Rahtu, E., Kannala, J., Blaschko, M.B.: Learning a category independent object detection cascade. In: ICCV (2011)

    Google Scholar 

  8. Zhang, Z., Warrell, J., Torr, P.H.S.: Proposal generation for object detection using cascaded ranking SVMs. In: CVPR, pp. 1497–1504 (2011)

    Google Scholar 

  9. Cheng, M.-M., Zhang, Z., et al.: BING: Binarized normed gradients for objectness estimation at 300fps. In: CVPR, pp. 3286–3293 (2014)

    Google Scholar 

  10. Zhao, Q., Liu, Z., Yin, B.: Cracking BING and beyond. In: BMVC (2014)

    Google Scholar 

  11. Zhang, Z., Liu, Y., et al.: BING++: a fast high quality object proposal generator at 100 fps. arXiv:1511.04511

  12. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_26

    Chapter  Google Scholar 

  13. Lu, C., Liu, S., Jia, J., Tang, C.K.: Contour box: rejecting object proposals without explicit closed contours. In: ICCV (2015)

    Google Scholar 

  14. Kuo, W., Hariharan, B., Malik, J.: DeepBox: learning objectness with convolutional networks. In: ICCV, pp. 2479–2487 (2015)

    Google Scholar 

  15. Hosang, J., Benenson, R., Dollar, P., Schiele, B.: What makes for effective detection proposals? IEEE TPAMI 38(4), 814–830 (2016)

    Article  Google Scholar 

  16. Chen, X., Ma, H., Wang, X., Zhao, Z.: Improving object proposals with multi-thresholding straddling expansion. In: CVPR, pp. 2587–2595 (2015)

    Google Scholar 

  17. Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: CVPR, pp. 3241–3248 (2010)

    Google Scholar 

  18. Zhang, L., Lin, L., Liang, X., He, K.: Is faster R-CNN doing well for pedestrian detection? In: ECCV, pp. 443–457 (2016)

    Google Scholar 

  19. Humayun, A., Li, F., Rehg, J.M.: RIGOR: reusing inference in graph cuts for generating object regions. In: CVPR, pp. 336–343 (2014)

    Google Scholar 

  20. van de Sande, K.E.A., Uijlings, J.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: ICCV (2011)

    Google Scholar 

  21. Verbickas, R., Laganiere, R., et al.: SqueezeMap: fast pedestrian detection on a low-power automotive processor using efficient convolutional neural networks. In: CVPRW, pp. 146–154 (2017)

    Google Scholar 

  22. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)

    Google Scholar 

  23. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59(2), 167–181 (2004)

    Article  Google Scholar 

  24. Arbeláez, P., Pont-Tuset, J., Barron, J.T., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR, pp. 328–335 (2014)

    Google Scholar 

  25. Krähenbühl, P., Koltun, V.: Geodesic object proposals. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 725–739. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_47

    Chapter  Google Scholar 

  26. Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: NIPS, pp. 2553–2561 (2013)

    Google Scholar 

  27. Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: CVPR, pp. 2155–2162 (2014)

    Google Scholar 

  28. Sermanet, P., Eigen, D., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks, CoRR, abs/1312.6229 (2013)

    Google Scholar 

  29. Zhang, Y., Sohn, K., Villegas, R., Pan, G., Lee, H.: Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction. In: CVPR, pp. 249–258 (2015)

    Google Scholar 

  30. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 (2014)

  31. Russakovsky, O., Deng, J., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  32. Liu, W., Rabinovich, A., Berg, A.C.: Parsenet: Looking wider to see better, arXiv:1506.04579 (2015)

  33. Chen, L.C., Papandreou, G., et al.: Semantic image segmentation with deep convolutional nets and fully connected crfs, arXiv:1412.7062 (2014)

  34. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

    Google Scholar 

  35. Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)

    Article  MathSciNet  Google Scholar 

  36. http://xgboost.readthedocs.io/en/latest/

  37. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACMMM, pp. 675–678 (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Lu Ding

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Yong Wang & Robert Laganière

  3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Xinbin Luo & Shan Fu

Authors
  1. Lu Ding
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  2. Yong Wang
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  3. Robert Laganière
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  4. Xinbin Luo
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  5. Shan Fu
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Corresponding author

Correspondence to Robert Laganière .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, USA

    Richard Boyle

  3. University of Nevada, Reno, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, USA

    Darko Koracin

  5. DARPA, Arlington, USA

    Matt Turek

  6. University of Utah, Salt Lake City, USA

    Srikumar Ramalingam

  7. National University of Defense Technology, Changsha, China

    Kai Xu

  8. Microsoft Research Asia, Beijing, China

    Stephen Lin

  9. Bosch Research, Farmington Hills, MI, USA

    Bilal Alsallakh

  10. University of North Carolina at Charlotte, Charlotte, USA

    Jing Yang

  11. Microsoft Research, Redmond, USA

    Eduardo Cuervo

  12. University of Colorado at Colorado Springs, Colorado Springs, USA

    Jonathan Ventura

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Ding, L., Wang, Y., Laganière, R., Luo, X., Fu, S. (2018). Scale-Aware RPN for Vehicle Detection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2018. Lecture Notes in Computer Science(), vol 11241. Springer, Cham. https://doi.org/10.1007/978-3-030-03801-4_43

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  • DOI: https://doi.org/10.1007/978-3-030-03801-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03800-7

  • Online ISBN: 978-3-030-03801-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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