Abstract
Taking the characteristic value as the core, a population abnormality detection algorithm is used to process the crowd surveillance video. Using density detection, the density of the population is first obtained. Object-based feature extraction is used in low-density scenes, and pixel-based feature extraction in high-density scenes. So as to obtain the crowd of exercise intensity, trajectory gradient, entropy and local density and other characteristic value. Finally identify the abnormal behavior of the population based on characteristic value. The experimental results show that the characteristic value is obvious when the abnormality occurs. The algorithm’s performance index is superior to the traditional crowd behavior recognition algorithm with high recognition rate.
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Acknowledgments
We thank our advisor, Professor Li Kangshun, for generously offers help in both software and hardware facilities, and supports us laboratory for research on our project. This work is supported by Ministry of Education of the People’s Republic of China for the National Student’s Training Program for Innovation and Entrepreneurship, “The Detection of Crowd Behavior Based on Deep Learning”. This work was jointly supported by Natural Science Foundation of Guangdong Province of China (#2017A010101037), and Natural Science Foundation of China (#61573157 and #61703170).
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Li, K., Huang, H., Zheng, Z., Lu, Y. (2018). Research of Crowed Abnormal Behavior Detection Technology Based on Trajectory Gradient. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_43
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DOI: https://doi.org/10.1007/978-981-13-1651-7_43
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