Research of Crowed Abnormal Behavior Detection Technology Based on Trajectory Gradient
| Autor: | Kangshun Li, Lu Yusheng, Hongtao Huang, Zebiao Zheng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
education.field_of_study
genetic structures business.industry Computer science Feature extraction Population ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Population based Performance index Entropy (information theory) Artificial intelligence Abnormality business Crowd psychology Recognition algorithm education |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811316500 |
| DOI: | 10.1007/978-981-13-1651-7_43 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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