11,939 research outputs found
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification
Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR)
person re-identification (Re-ID) is achieved by projecting them into a common
space, allowing person Re-ID in 24-hour surveillance systems. However, with
respect to the probe-to-gallery, almost all existing RGB-IR based cross-modal
person Re-ID methods focus on image-to-image matching, while the video-to-video
matching which contains much richer spatial- and temporal-information remains
under-explored. In this paper, we primarily study the video-based cross-modal
person Re-ID method. To achieve this task, a video-based RGB-IR dataset is
constructed, in which 927 valid identities with 463,259 frames and 21,863
tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed
dataset, we prove that with the increase of frames in a tracklet, the
performance does meet more enhancement, demonstrating the significance of
video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is
further proposed, which not only projects two modalities to a modal-invariant
subspace, but also extracts the temporal-memory for motion-invariant. Thanks to
these two strategies, much better results are achieved on our video-based
cross-modal person Re-ID. The code and dataset are released at:
https://github.com/VCMproject233/MITML
Automatic Synchronization of Multi-User Photo Galleries
In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi
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