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In order to solve the problem of insufficient feature extraction of human dynamic skeleton features in abnormal behavior recognition, an unsupervised abnormal behavior recognition method based on enhanced spatiotemporal graph normalization flow was proposed. Transformer and convolution block attention module were employed to enhance the feature expression capability of the model and the performance of the abnormal behavior recognition algorithm in the global and spatiotemporal domains. Firstly, the Transformer module was incorporated into the affine layer of the normalized flow to augment the efficacy of dynamic skeleton feature information at the global level. Subsequently, the convolution attention was introduced into the convolution module of space and time graphs respectively to effectively enhance the spatial and temporal representation of dynamic skeleton features. Finally, simulation verification was conducted on the ShanghaiTech and UBnormal datasets, and the recognition accuracy attains 86.4% and 70.2% respectively, thereby demonstrating the effectiveness of the method.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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