Abstract
To meet the needs of transportation systems for smart scenic security services, real-time detection and identification of traffic anomalies with high accuracy is essential. Based on the multi-objective sparse optical flow estimation method based on KLT algorithm, an improved algorithm for robust sparse optical flow is designed. The Forward-Backward error calculation method was used to eliminate the error optical flow generated by the KLT algorithm and the robustness of optical flow was improved. The proposed algorithm was verified by the actual traffic scene monitoring example, and the anomaly detection accuracy is above 80%. Furthermore, it has good detection effect on the benchmark dataset.
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Acknowledgments
The work is supported by Xaar Network Next Generation Internet Technology Innovation Project(No.NGII20180901), and the Major special project of science and technology of Guangxi(No.AA18118047-7).
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Wang, K., Huang, W., Chen, Y., Chen, N., He, Z. (2020). Intelligent Multi-objective Anomaly Detection Method Based on Robust Sparse Flow. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_33
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DOI: https://doi.org/10.1007/978-981-15-7984-4_33
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