Partial attention and multi-attribute learning for vehicle re-identification

Tumrani, Saifullah; Deng, Zhiyi; Lin, Haoyang; Shao, Jie

doi:10.1016/j.patrec.2020.07.034

Cited by 24 publications

(9 citation statements)

References 18 publications

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“…e multiscale attention framework (MSA) proposed by Zheng et al [14], which considers multiscale mechanisms and attention technologies, uses attention blocks on subnetworks of each scale to mine complementary and distinguishing information. Tumrani et al [25] proposed local attention and multiple attributes based on appearance features for vehicle reidentification, improved the number of output channels of the deconvolution head network proposed by Xiao et al [24], and realized the use of vehicle key points to obtain local features.…”

Section: Multiattention Models In Person and Vehicle Re-idmentioning

confidence: 99%

“…DFN [26] used fine-grained network to obtain global and local features for vehicle identification. STR + ST [36] and PAMA [25] separately process the key points of the vehicle. e former extracts 20 well-aligned combination key points of local area features in different directions.…”

Section: Performance On the Veri-776 Datasetmentioning

confidence: 99%

“…e former extracts 20 well-aligned combination key points of local area features in different directions. PAMA [25] used multiple vehicle attributes and key points to extract vehicle features. VRSDNet [37] used the short and dense connection mechanism of the Siamese network to learn vehicle characteristics.…”

Section: Performance On the Veri-776 Datasetmentioning

confidence: 99%

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Vehicle Reidentification Based on MAPANet and k-Reciprocal Encoding

Tian

2021

Scientific Programming

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Vehicle reidentification has important applications in intelligent monitoring systems. However, due to many factors, such as inaccurate vehicle image detection and viewing angle changes, distinguishing features cannot be effectively obtained when the vehicle is reidentified. To improve the recognition ability and robustness of vehicle reidentification, this study proposes a new multiattention part alignment network (MAPANet). The network uses different channels in the feature map to perceive different characteristics of the image clustering of the channels and achieves fine-grained attention to the vehicle. It can automatically locate the distinguishing subregions in the vehicle image and avoid the need for a large number of additional manual pretreatment steps. Moreover, an unsupervised reranking method based on multiple metrics is proposed. The k-reciprocal encoding algorithm can optimize the performance of the sorted list in the reordering problem, recalculate the interclass and intraclass distances of vehicle pictures, and improve sorting results. The experiments in this paper are carried out on the VeRi-776 and VehicleID datasets, and the mean average precision (mAP) results on the two datasets are 72.83% and 75.25%, respectively.

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Section: Multiattention Models In Person and Vehicle Re-idmentioning

confidence: 99%

Section: Performance On the Veri-776 Datasetmentioning

confidence: 99%

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Vehicle Reidentification Based on MAPANet and k-Reciprocal Encoding

Tian

2021

Scientific Programming

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“…It seeks to identify specific vehicles and match query vehicles across multiple non-overlapping cameras, which is convenient to find the target vehicle in a vast database. Vehicle re-id [1][2][3][4] is a critical visual task in intelligent transportation surveillance systems since it allows you to pinpoint a single vehicle precisely. Currently, most vehicle re-id task is performed utilizing videos captured by traffic surveillance cameras.…”

Section: Introductionmentioning

confidence: 99%

View-aware attribute-guided network for vehicle re-identification

et al. 2023

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Vehicle re-identification is one of the essential applications for intelligent transportation systems and urban surveillance. However, enormous variation in inter-class and intra-class resemblance creates a challenge for methods to distinguish between the same vehicles with different views. Additionally, diversified illumination and complicated environments create significant hurdles for the existing methods. We present a multi-guided learning method in this paper that uses multi-attribute and view point information, while also enhancing the robustness of feature extraction. The multi-attribute sub-network learns discriminative features like, i.e. color and type of vehicle. Moreover, the view predictor network adds extra information to the feature embedding and To validate the effectiveness of our framework, experiments on two benchmark datasets VeRi-776 and VehicleID are conducted. Experimental results illustrate our framework achieved comparative performance. VAAG CAR A CAR BFig. 1 The figure shows two separate cars from distinct views from VeRi-776. Car A on the top row illustrates several views from various angles. Car B in the lower row has a diversity of viewpoints as well. These cars have very similar appearances, and it's difficult to tell them apart. Car A appears to be different than its other views and identical to Car B from various perspectives.

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“…There is a substantial body of classification theory that encompasses decades of productive research trends [3,4]. Work on pattern categorization and other domains has increased as a result of the necessity to create automated systems in most industries [5][6][7][8][9]. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator

Saha¹,

Reddy²,

Kumar³

2022

JIMD

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The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation. Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created. One real-world medical dataset, namely Parkinson disease data set is used to test the proposed approaches. When compared to older existing operators, the new classifiers have better classification accuracy.

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Partial attention and multi-attribute learning for vehicle re-identification

Cited by 24 publications

References 18 publications

Vehicle Reidentification Based on MAPANet and k-Reciprocal Encoding

Vehicle Reidentification Based on MAPANet and k-Reciprocal Encoding

View-aware attribute-guided network for vehicle re-identification

A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator

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