Abstract
The vehicle detection and tracking in driving assistance system are ordinarily achieved by the optical or radar technology. In this work, we explore video processing for driving assistance system. An object’s detection and tracking system based on the Faster R-CNN and Camshift algorithm is proposed, and Kalman filtering algorithm is used to predict the position of objects. Our system differs with other object-tracking algorithms based on deep learning, which directly detects each frame and has no tracking part, such as YOLO and SSD. We also introduce an evaluation criterion for driving assistance system according to the dangerous level of the surrounding cars. Through the experiments on the videos recorded by drive recorder, we show that our approach can achieve better performance in complex scenes than YOLO or SSD and can satisfy the real-time requirement by processing 30 frames per second.
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References
Robert, K.: Video-based traffic monitoring at day and night vehicle features detection tracking. In: International IEEE Conference on Intelligent Transportation Systems, pp. 1–6. IEEE (2009)
Alshaqaqi, B., Baquhaizel, A.S., Ouis, M.E.A., et al.: Driver drowsiness detection system. In: International Workshop on Systems, Signal Processing and Their Applications, pp. 151–155. IEEE (2013)
Milan, A., Rezatofighi, S.H., Dick, A., et al.: Online multi-target tracking using recurrent neural networks (2016)
Jiapeng, W.U., Yang, Z., Jun, W.U., et al.: Virtual line group based video vehicle detection algorithm utilizing both luminance and chrominance. In: IEEE Conference on Industrial Electronics and Applications, pp. 2854–2858. IEEE (2007)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. Comput. Sci. 580–587 (2014)
He, K., Zhang, X., Ren, S., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Girshick, R.: Fast R-CNN. Comput. Sci. (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. (99), 1–1 (2015)
Fang, Q., Mei, X.C., Zhang, Y.P.: Design of Kalman filter for maneuvering target track. Radar Sci. Technol. (2006)
Qu, J.B., Qu, C.R., Wang, S.J.: Multi-objective forecast track based on Kalman-Camshift algorithm. Adv. Mater. Res. 211–212, 137–141 (2011)
Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detector (2016)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection, pp. 779–788 (2016)
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Yang, K., Zhang, C., Wu, M. (2019). Tracking System for Driving Assistance with the Faster R-CNN. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 760. Springer, Singapore. https://doi.org/10.1007/978-981-13-0344-9_31
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DOI: https://doi.org/10.1007/978-981-13-0344-9_31
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