Deep asymmetric video-based person re-identification
Pattern Recognition, ISSN: 0031-3203, Vol: 93, Page: 430-441
2019
- 24Citations
- 18Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In this paper, we investigate the problem of video-based person re-identification (re-id) which matches people’s video clips across non-overlapping camera views at different time. A key challenge of video-based person re-id is a person’s appearance and motion would always display differently and take effects unequally at disjoint camera views due to the change of lighting, viewpoint, background and etc., which we call the “view-bias” problem. However, many previous video-based person re-id approaches have not quantified the importance of different types of features at different camera views, so that the two types of important features (i.e. appearance and motion features) do not collaborate effectively and thus the “view-bias” problem remains unsolved. To address this problem, we propose a Deep Asymmetric Metric learning (DAM) method that embeds a proposed asymmetric distance metric learning loss into a two-stream deep neural network for jointly learning view-specific and feature-specific transformations to overcome the “view-bias” problem in video-based person re-id. As learning these view-specific transformations become expensive when there are large amount of camera views, a clustering-based DAM method is developed to make our DAM scalable. Extensive evaluations have been carried out on three public datasets: PRID2011, iLIDS-VID and MARS. Our results verify that learning view-specific and feature-specific transformations are beneficial, and the presented DAM has empirically performed more effectively overall for video-based person re-id on challenging benchmarks.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0031320319301487; http://dx.doi.org/10.1016/j.patcog.2019.04.008; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85065235883&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0031320319301487; https://api.elsevier.com/content/article/PII:S0031320319301487?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0031320319301487?httpAccept=text/plain; https://dul.usage.elsevier.com/doi/; https://dx.doi.org/10.1016/j.patcog.2019.04.008
Elsevier BV
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