Discriminative feature and dictionary learning with part-aware model for vehicle re-identification

Wang, Huibing; Peng, Jinjia; Jiang, Guangqi; Xu, Fengqiang; Fu, Xianping

doi:10.1016/j.neucom.2020.06.148

Cited by 57 publications

(14 citation statements)

References 20 publications

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“…The design of existing Re-ID methods is mainly based on handcrafted features [ 14 , 15 ], metric learning [ 16 , 17 , 18 ] and deep learning networks [ 5 , 9 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. Some recent approaches learn features at the part level and achieve state-of-the-art performance in Re-ID tasks.…”

Section: Related Workmentioning

confidence: 99%

Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

Pang

Yin

Zheng

2023

Entropy

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Vehicle re-identification across multiple cameras is one of the main problems of intelligent transportation systems (ITSs). Since the differences in the appearance between different vehicles of the same model are small and the appearance of the same vehicle changes drastically from different viewpoints, vehicle re-identification is a challenging task. In this paper, we propose a model called multi-receptive field soft attention part learning (MRF-SAPL). The MRF-SAPL model learns semantically diverse vehicle part-level features under different receptive fields through multiple local branches, alleviating the problem of small differences in vehicle appearance. To align vehicle parts from different images, this study uses soft attention to adaptively locate the positions of the parts on the final feature map generated by a local branch and maintain the continuity of the internal semantics of the parts. In addition, to obtain parts with different semantic patterns, we propose a new loss function that punishes overlapping regions, forcing the positions of different parts on the same feature map to not overlap each other as much as possible. Extensive ablation experiments demonstrate the effectiveness of our part-level feature learning method MRF-SAPL, and our model achieves state-of-the-art performance on two benchmark datasets.

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Section: Related Workmentioning

confidence: 99%

Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

Pang

Yin

Zheng

2023

Entropy

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“…The field of fine-grained recognition [45,44,46,47] has been widely studied. The early part-based method used additional strong supervision information to identify object categories, such as part annotations and bounding box annotations.…”

Section: Fine-grained Recognitionmentioning

confidence: 99%

CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data

Zhang,

Fan,

Huang

et al. 2022

Preprint

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Data augmentation has been proved effective in training deep models. Existing data augmentation methods tackle finegrained problem by blending image pairs and fusing corresponding labels according to the statistics of mixed pixels, which produces additional noise harmful to the performance of networks. Motivated by this, we present a simple yet effective cross ensemble knowledge distillation (CEKD) model for fine-grained feature learning. We innovatively propose a cross distillation module to provide additional supervision to alleviate the noise problem, and propose a collaborative ensemble module to overcome the target conflict problem. The proposed model can be trained in an end-to-end manner, and only requires image-level label supervision. Extensive experiments on widely used fine-grained benchmarks demonstrate the effectiveness of our proposed model. Specifically, with the backbone of ResNet-101, CEKD obtains the accuracy of 89.59%, 95.96% and 94.56% in three datasets respectively, outperforming state-of-the-art API-Net by 0.99%, 1.06% and 1.16%.

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“…Li et al [7] proposed a DJDL model that utilized a deep convolutional network to effectively extract discriminative representations for vehicle images. Wang et al [8] proposed the Triplet Center Loss based Part-aware Model (TCPM) that leverages the discriminative features in part details of vehicles to refine the accuracy. Zhou et al [9] learn transformations across different viewpoints of vehicles by the proposed model which is combined with CNN and LSTM.…”

Section: The Status Of Researchmentioning

confidence: 99%

Vehicle Matching Based on Similarity Metric Learning

Li¹,

Deng²,

Zhou³

2022

Journal of New Media

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With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e., Day pattern or Night pattern) for vehicle matching. To exploit properties and relationships among vehicle datasets, we first apply the domain transformer to translate the domain of vehicle images, and then utilize the two-channel Siamese network to extract features from both domains for better feature similarity learning. Experimental results illustrate that our models achieve improvements over state-of-the-arts.

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Discriminative feature and dictionary learning with part-aware model for vehicle re-identification

Cited by 57 publications

References 20 publications

Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

Multi-Receptive Field Soft Attention Part Learning for Vehicle Re-Identification

CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data

Vehicle Matching Based on Similarity Metric Learning

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