Abstract
With the development of the city, the traffic crowd congested scene is increasing frequency. And the traffic crowd congested may bring disaster. It is important for city traffic management to recognize traffic crowd congested scene. However, the traffic crowd scene is dynamically and the visual scales are varied. Due to the multi-scale problem, it is hard to distinguish the congested traffic crowd scene. To solve the multiple scales problem in traffic crowd congested scene recognition, in this paper, a traffic crowd congested scene recognition method based on dilated convolution network is proposed, which combines the dilated convolution and VGG16 network for traffic crowd congested scene recognition. To verify the proposed method, the experiments are implemented on two crowd datasets including the CUHK Crowd dataset and Normal-abnormal Crowd dataset. And the experimental results are compared with three states of the art methods. The experimental results demonstrate that the performance of the proposed method is more effective in congested traffic crowd scene recognition. Compared with the three state of the art methods, the average accuracy value, and the average AUC values of the proposed method are improved by 15.87\(\%\) and 11.58\(\%\) respectively.
This work was supported in part by the Central Public Research Institutes Special Basic Research Foundation No. 2020–9004, No. 2020–9065, in part by the National Natural Science Foundation of China under Grant Nos. 61672082 and 61822101, in part by Beijing Municipal Natural Science Foundation No. 4181002.
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Acknowledgements
This work was supported in part by the Central Public Research Institutes Special Basic Research Foundation No. 2020–9004 and No. 2020–9065, in part by the National Natural Science Foundation of China under Grant Nos. 61672082 and 61822101, in part by Beijing Municipal Natural Science Foundation No. 4181002.
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Wei, X., Liu, Y., Zhou, W., Xia, H., Tian, D., Cheng, R. (2021). Traffic Crowd Congested Scene Recognition Based on Dilated Convolution Network. In: Gao, W., et al. Intelligent Computing and Block Chain. FICC 2020. Communications in Computer and Information Science, vol 1385. Springer, Singapore. https://doi.org/10.1007/978-981-16-1160-5_12
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DOI: https://doi.org/10.1007/978-981-16-1160-5_12
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