Abstract
At present, the development of intelligent vehicles is a global trend, and vehicular collision estimation technology based on machine vision has become an important topic in current academic research, in which the vehicle detection problem in a general environment has attracted much attention. This paper presents a new method for vehicle detection as well as driving orientation estimation based on deformed oriented bounding box, and to predict conflict points in the vehicular surroundings based on cascade convolutional networks. Extensive experiments show that our approach provides a new solution for detecting vehicles, which could be used for further collision estimation.
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References
Gao, F., Duan, J., He, Y., Wang, Z.: A test scenario automatic generation strategy for intelligent driving systems. Math. Probl. Eng. 2019, 1–10 (2019)
Cao, J., Song, C., Peng, S., Xiao, F., Song, S.: Improved traffic sign detection and recognition algorithm for intelligent vehicles. Sensors 19(18), 4021 (2019)
Masaki, I.: Vision-Based Vehicle Guidance. Springer Science & Business Media (2012)
Wang, X., Tang, J., Niu, J., Zhao, X.: Vision-based two-step brake detection method for vehicle collision avoidance. Neurocomputing 173, 450–461 (2016)
Zhang, W., et al.: Deep learning-based real-time fine-grained pedestrian recognition using stream processing. IET Intel. Transp. Syst. 12(7), 602–609 (2018)
Chen, X., Xiang, S., Liu, C.L., Pan, C.H.: Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 11(10), 1797–1801 (2014)
Zhou, Y., Nejati, H., Do, T.T., Cheung, N.M., Cheah, L.: Image-based vehicle analysis using deep neural network: a systematic study. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), pp. 276–280. IEEE (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Tang, T., Zhou, S., Deng, Z., Zou, H., Lei, L.: Vehicle detection in aerial imagesbased on region convolutional neural networks and hard negative example mining. Sensors 17(2), 336 (2017)
Wang, H., Cai, Y., Chen, L.: A vehicle detection algorithm based on deep belief network. Sci. World J. 2014, 1–7 (2014)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for ac-curate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3d model views. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2686–2694 (2015)
Poirson, P., Ammirato, P., Fu, C.Y., Liu, W., Kosecka, J., Berg, A.C.: Fast singleshot detection and pose estimation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 676–684. IEEE (2016)
Mousavian, A., Anguelov, D., Flynn, J., Kosecka, J.: 3d bounding box estimation using deep learning and geometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7074–7082 (2017)
Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2930–2940 (2013)
Jazayeri, A., Cai, H., Zheng, J.Y., Tuceryan, M.: Vehicle detection and tracking in car video based on motion model. IEEE Trans. Intell. Transp. Syst. 12(2), 583–595 (2011)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879–2886. IEEE (2012)
Hubbard, P.M.: Approximating polyhedra with spheres for time-critical collision detection. ACM Trans. Graph. 15(3), 179–210 (1996)
Gottschalk, S., Lin, M.C., Manocha, D.: Obbtree: a hierarchical structure for rapid interference detection. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, pp. 171–180 (1996)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Sarika, K.S., Sudha, P.: An analysis of edge extraction for MRI medical images through mathematical morphological operators approaches. Int. J. Comput. Appl. 9–13 (2013)
Harris, C., Stephens, M., et al.: A combined corner and edge detector. In: Alvey Vision Conference, vol. 15, pp. 10–5244. Citeseer (1988)
Shi, J.: Good features to track. In: 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 593–600. IEEE (1994)
Acknowledgment
The authors appreciate the Institute of highway ministry of transport’s assistance in providing test images. Also, we thank Prof. Nelson Max in University of California, Davis for performing technical editing and language editing.
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Yang, W., Park, M., Song, X., Ling, S., Li, Y., Gu, X. (2022). Vehicle Detection Based on Cascade Deep Learning Method Using Deformed Oriented Bounding Box. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_55
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DOI: https://doi.org/10.1007/978-3-030-97546-3_55
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