Joint Feature and Similarity Deep Learning for Vehicle Re-identification
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The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously and is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.Abstract:Â
In this paper, a joint feature and similarity deep learning (JFSDL) method for vehicle re-identification is proposed The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously The siamese deep network is learned under the joint identification and verification supervision The joint identification and verification supervision is realized by linearly combining two softmax functions and one hybrid similarity learning function Moreover, based on the hybrid similarity learning function, the similarity score between the input vehicle image pair is also obtained by simultaneously projecting the element-wise absolute difference and multiplication of the corresponding deep learning feature pair with a group of learned weight coefficients Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data setsread more
Citations
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Journal ArticleDOI
A Survey of Vehicle Re-Identification Based on Deep Learning
Hongbo Wang,Jiaying Hou,Na Chen +2 more
TL;DR: This survey gives a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and compares them from characteristics, advantages, and disadvantages.
Journal ArticleDOI
Deep Quadruplet Appearance Learning for Vehicle Re-Identification
TL;DR: Extensive experiments conducted on two commonly used datasets VeRi-776 and VehicleID have demonstrated that the proposed DQAL approach outperforms multiple recently reported vehicle Re-ID methods.
Journal ArticleDOI
Multi-label learning with multi-label smoothing regularization for vehicle re-identification
TL;DR: The proposed MLL with MLSR approach can effectively improve the performance delivered by the baseline and outperform multiple state-of-the-art vehicle re-ID methods as well.
Journal ArticleDOI
Uncertainty-optimized deep learning model for small-scale person re-identification
TL;DR: This study considers the uncertainty of pedestrian representation for small-scale person re-identification and designs an improved Monte Carlo strategy that considers both the average distance and shortest distance for matching and ranking.
Journal ArticleDOI
Multi-Label-Based Similarity Learning for Vehicle Re-Identification
Saghir Ahmed Saghir Alfasly,Yongjian Hu,Haoliang Li,Liang Tiancai,Xiaofeng Jin,Liu Beibei,Zhao Qingli +6 more
TL;DR: This paper proposes multi-label-based similarity learning (MLSL) for vehicle re-identification obtaining an efficient deep-learning-based model that derives robust vehicle representations and proves the superiority of the model over multiple state-of-the-art methods on the three mentioned datasets.
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