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- W4387682227 abstract "Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework, <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>GroupTransformer</i> , for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>We will release the code for research purposes</i> ." @default.
- W4387682227 created "2023-10-17" @default.
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- W4387682227 date "2023-01-01" @default.
- W4387682227 modified "2023-10-17" @default.
- W4387682227 title "Towards Grouping in Large Scenes with Occlusion-aware Spatio-temporal Transformers" @default.
- W4387682227 doi "https://doi.org/10.1109/tcsvt.2023.3324868" @default.
- W4387682227 hasPublicationYear "2023" @default.
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