发微光
发表于 2025-3-28 14:36:34
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Cardioversion
发表于 2025-3-28 22:12:25
Gong-Ru Lin,Yu-Chuan Su,Yu-Chieh Chimatching people across cameras with different viewpoints and lighting conditions, as well as across human pose variations. The literature has since devised several approaches to tackle these challenges, but the vast majority of the work has been concerned with appearance-based methods. We propose an
Defiance
发表于 2025-3-28 23:33:59
https://doi.org/10.1007/978-94-017-9392-6s of the discriminating power of their characteristic features. In our approach, we first segment the pedestrian images into meaningful parts, then we extract features from such parts as well as from the whole body and finally, we perform a salience analysis based on regression coefficients. Given a
天然热喷泉
发表于 2025-3-29 05:10:17
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反感
发表于 2025-3-29 09:05:57
Ann Marie Ryan,Charles Tocci,Seungho Moones multiple local single target trackers to hypothesise short term tracks. These tracks are combined with the tracks obtained by a global multi-target tracker, if they result in a reduction in the global cost function. Since tracking failures typically arise when targets become occluded, we propose
WITH
发表于 2025-3-29 14:06:41
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escalate
发表于 2025-3-29 16:18:35
Natural Resources and Sustainability, surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous metric learning methods have been proposed for person re-identification in recent years, most of them suffer from the small sample size (SSS) problem because
后退
发表于 2025-3-29 23:04:58
,What’s Your Innovation Process?,ical role in underpinning many multi-camera surveillance tasks. A fundamental assumption in almost all existing re-identification research is that cameras are in fixed emplacements, allowing the explicit modelling of camera and inter-camera properties in order to improve re-identification. In this p
CODA
发表于 2025-3-30 01:43:38
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ATP861
发表于 2025-3-30 05:38:51
Conference proceedings 2015njunction with the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014..The 203 workshop papers were carefully reviewed and selected for inclusion in the proceedings. They where presented at workshops with the following themes: where computer vision