266 research outputs found
Re-ranking person re-identification with k-reciprocal encoding
© 2017 IEEE. When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k- reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for reranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the largescale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method1
Spatial-Temporal Person Re-identification
Most of current person re-identification (ReID) methods neglect a
spatial-temporal constraint. Given a query image, conventional methods compute
the feature distances between the query image and all the gallery images and
return a similarity ranked table. When the gallery database is very large in
practice, these approaches fail to obtain a good performance due to appearance
ambiguity across different camera views. In this paper, we propose a novel
two-stream spatial-temporal person ReID (st-ReID) framework that mines both
visual semantic information and spatial-temporal information. To this end, a
joint similarity metric with Logistic Smoothing (LS) is introduced to integrate
two kinds of heterogeneous information into a unified framework. To approximate
a complex spatial-temporal probability distribution, we develop a fast
Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint,
the st-ReID model eliminates lots of irrelevant images and thus narrows the
gallery database. Without bells and whistles, our st-ReID method achieves
rank-1 accuracy of 98.1\% on Market-1501 and 94.4\% on DukeMTMC-reID, improving
from the baselines 91.2\% and 83.8\%, respectively, outperforming all previous
state-of-the-art methods by a large margin.Comment: AAAI 201
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