Dual attention granularity network for vehicle re-identification

Zhang, Jianhua; Cao, Jiewei; Liu, Ruyu; Bian, Linjie; Chen, Shengyong

doi:10.1007/s00521-021-06559-6

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…TCPM [ 25 ] divides the final feature map from the horizontal and vertical directions and uses an external memory module to store partial features to model the global feature vector. Dual+SA [ 41 ] uses self-attention to generate attention maps about the vehicle model and vehicle ID and inputs the attention map to the part localization module to obtain the fine region features of ROIs. Relying only on visual information, our proposed model MRF-SAPL achieves 81.5% mAP, 94.7% Top-1 accuracy, and 98.7% Top-5 accuracy.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

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

Pang

Yin

Zheng

2023

Entropy

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“…A dual attention re-identification network is proposed by authors in [63] that selectively scores the vehicle parts with higher attention scores. The framework extracts the vehicle features by using a dual branch CNN network.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

et al. 2023

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Vehicle re-identification is an important feature of an intelligent transportation system as part of a smart city application. Vehicle re-identification aims at matching vehicles from images acquired by surveillance cameras at different locations. During rush hours, vehicles are densely occupied across regions such as entry/exit of gated campuses, railways, airports, educational institutions, etc. Due to this uneven flow of traffic, there is a possibility of violation of traffic rules by the vehicles that lead to a security breach. In such scenarios to speed up the re-identification process, it is justified to look into a specific group of surveillance cameras to detect and re-identify vehicles on day to day basis in near real-time. However, the existing vehicle re-identification datasets do not contain zone specific information and therefore can not be used to evaluate the performance of re-identification algorithms in different zones. In the proposed work for re-identification, a framework is developed that performs vehicle re-identification across a group of cameras that monitors varying traffic movements over an area. These areas defined as "strategic zones" comprise a subset of non-overlapping cameras that are installed to monitor non-uniform vehicle movements. The reidentification framework is evaluated on a novel dataset developed to understand the performance of vehicle re-identification across strategic zones. The dataset consists of videos of vehicles captured through 20 CCTV surveillance cameras that are grouped into four different zones. Various experiments are conducted to study the performance of re-identification across four zones using a deep neural network with triplet loss, L2 regularization, and re-ranking. The experiments conducted with an image dimension of 224 × 224 have demonstrated an overall mAP of 77.22%. Also, for each of the four zones a mAP of 82.16%, 69.1%, 66.5%, and 75.76% is achieved. The experimental results demonstrate huge variations in the accuracy of vehicle re-identification method across different zones. Therefore, the study assess the possible measures that can be taken to improve the performance in individual zones for an accurate vehicle re-identification in intelligent transport system.

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“…( 1), the input of the attention layer is the output of Conv1D layer, which has the shape of R F ×T , F stands for the number of filters, also called channels. In Conv1D layer, all channels have the same importance, and this leads to the loss of important information [27], [28]. Therefore, the attention mechanism is designed for the selection of important channels.…”

Section: B Cluster-based Federated Learningmentioning

confidence: 99%

A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

Cheng

Tian

Shi

et al. 2022

IEEE Trans. Ind. Inf.

Self Cite

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Wind energy is a fast-growing renewable energy but faces the blade icing. Data-driven methods provide talented solutions for blade icing detection but a considerable amount of IoT data need to be collected to a central server, which may lead to the leakage of sensitive business data. To address this limitation, this work proposes BLADE, a Blockchainempowered imbalanced federated learning (FL) model for blade icing detection. With the help of Blockchain, the conventional FL is improved without worrying about the failure of the single centralized server and boosts the privacy-preserving. A validation mechanism is introduced into the Blockchain to enhance the defense of poisoning attacks. In addition, a novel imbalanced learning algorithm is integrated into BLADE to solve the classimbalance problem in the sensor data. The BLADE is evaluated on the 10 wind turbines from two wind farms. The experimental results verify the effectiveness, superiority, and feasibility of the proposed BLADE.

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Dual attention granularity network for vehicle re-identification

Cited by 8 publications

References 39 publications

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

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

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

A Blockchain-Empowered Cluster-Based Federated Learning Model for Blade Icing Estimation on IoT-Enabled Wind Turbine

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