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Augmented inputs for surveillance re-identification
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DOI：10.1007/s13735-023-00309-1.png)
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摘要
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Person Re-Identification (Re-ID) is one of the important applications for Surveillance. However, the performance of Re-ID is dependent on the input quality, which cannot be guaranteed from the surveillance systems. We explored the technique from Gait Re-ID to address viewpoint changes. From our findings, adding horizontally mirrored image into an auxiliary pipeline can achieve a modest performance uplift in our test (0.8% net increase in mean average precision and 0.9% increase in Rank-1 accuracy) in MARS dataset. This extra pipeline can be substituted by Heterogeneous Input Triplet Loss (hiTri) for minimal performance loss. The overall performance of the proposed method outperforms state-of-the-art techniques on well-known datasets. Further investigations on other auxiliary input types are warranted.
Keyword:
Biometrics
Pattern recognition
Surveillance
Video-based person re-identification
期刊
IF:
2.9
论文数:
279
被引数:
866
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