CARF-net : CNN attention and RNN fusion network for video-based person reidentification
Video-based person reidentification is a challenging and important task in surveillance-based applications. Toward this, several shallow and deep networks have been proposed. However, the performance of existing shallow networks does not generalize well on large datasets. To improve the generalizati...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/105466 http://hdl.handle.net/10220/48712 http://dx.doi.org/10.1117/1.JEI.28.2.023036 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Video-based person reidentification is a challenging and important task in surveillance-based applications. Toward this, several shallow and deep networks have been proposed. However, the performance of existing shallow networks does not generalize well on large datasets. To improve the generalization ability, we propose a shallow end-to-end network which incorporates two stream convolutional neural networks, discriminative visual attention and recurrent neural network with triplet and softmax loss to learn the spatiotemporal fusion features. To effectively use both spatial and temporal information, we apply spatial, temporal, and spatiotemporal pooling. In addition, we contribute a large dataset of airborne videos for person reidentification, named DJI01. It includes various challenging conditions, such as occlusion, illuminationchanges, people with similar clothes, and the same people on different days. We perform elaborate qualitative and quantitative analyses to demonstrate the robust performance of the proposed model. |
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