Abstract
With the rapid growth of traffic video data, it is necessary to improve the computing power and accuracy of image processing. In this paper, a video vehicle detection and recognition system based on MapReduce and convolutional neural network is proposed to reduce the time-consume and improve the recognition accuracy in video analysis. First, a fast and reliable deep learning algorithm based on YOLOv2 is used to detect vehicle in real-time. And then the license plate recognition algorithm based on improved convolutional neural network is presented to recognize the license plate image extracted from the detected vehicle region. Finally, the Hadoop Video Processing Interface (HVPI) and MapReduce framework are combined to apply the video vehicle detection and recognition algorithms for parallel processing. Experimental results are presented to verify that the proposed scheme has advantages of high detection rate and high recognition accuracy, and strong ability of data processing in large-scale video data.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61561014) and the Innovation Project of GUET Graduate Education (No. 2016YJCX93 & No. 2018YJCX29).
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Chen, M., Wang, W., Dong, S., Zhou, X. (2018). Video Vehicle Detection and Recognition Based on MapReduce and Convolutional Neural Network. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_53
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DOI: https://doi.org/10.1007/978-3-319-93818-9_53
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