Video crowd counting method based on conv-pooling deep spatial and temporal features
Due to angle of camera,background,population density distribution and occlusion limitations,traditional video crowd counting methods based on underlying visual features are often difficult to achieve ideal results.Using the temporal and spatial features of video and conv-pooling method,high-level vi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2018-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2018161/ |
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| Summary: | Due to angle of camera,background,population density distribution and occlusion limitations,traditional video crowd counting methods based on underlying visual features are often difficult to achieve ideal results.Using the temporal and spatial features of video and conv-pooling method,high-level visual features were formed,local feature aggregation descriptors were used for quantization and codebook calculation to achieve accurate description of video crowd information.This method made full use of video motion and appearance information.Based on convolutional neural networks and pooling methods,the ability to describe video intrinsic attributes and features was improved.Experimental results show that the proposed method has higher precision and better robustness than traditional video crowd counting methods. |
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| ISSN: | 1000-0801 |