منابع مشابه
Temporal Model Adaptation for Person Re-identification
Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with ...
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Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting. It is challenging to incrementally optimize the models by using the abundant unlabeled data collected from the target do...
متن کاملPart-based spatio-temporal model for multi-person re-identification
0167-8655/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.patrec.2011.09.005 ⇑ Corresponding author. E-mail addresses: [email protected] (A. Beda h.edu (S.K. Shah). In this paper we propose an adaptive part-based spatio-temporal model that characterizes person’s appearance using color and facial features. Face image selection based on low level cues is used to select usable face images...
متن کاملTemporal Model Adaptation for Person Re-Identification: Supplementary Material
This supplementary material accompanies the paper entitled “Temporal Model Adaptation for Person Re-Identification”, accepted for publication in ECCV 2016. It introduces an additional analysis of our approach and experiments on the 3DPeS and the CUHK03 datasets which, due to page limit constraints, could have not been included in the main paper. It also provides a complete derivation of both th...
متن کاملVideo Person Re-identification by Temporal Residual Learning
In this paper, we propose a novel feature learning framework for video person re-identification (re-ID). The proposed framework largely aims to exploit the adequate temporal information of video sequences and tackle the poor spatial alignment of moving pedestrians. More specifically, for exploiting the temporal information, we design a temporal residual learning (TRL) module to simultaneously e...
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33018933