Vehicle re-identification using multi-task deep learning network and spatio-temporal model

Peng, Jinjia; Hao, Yun; Xu, Fengqiang; Fu, Xianping

doi:10.1007/s11042-020-09356-w

Cited by 4 publications

(2 citation statements)

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“…EALN [53] is able to generate localized samples in embedding space. MDLSTM [54] used deep model for discriminative features learning, and spatio-temporal constraints based model . MSA [20] introduced multi-scale attention based method to fuse discriminative features and global information.…”

Section: Comparasion With Other Methodsmentioning

confidence: 99%

“…The results of our proposed model on VeRi-776 dataset along with comparison of our model with recent state-of-the-art methods are listed in Table 2. The compared methods includes: LOMO [47]; BOW-CN [48]; GoogLeNet [49]; FACT [27]; DGD [50]; XVGAN [2]; two method CCL, and Mixed Differ-ence+CCL [28]; VAMI [16]; DenseNet121 [51]; ABLN-Ft-16 [52]; OIFE [31]; PAMAL [3]; PROVID [22]; VRSDnet [21]; EALN [53]; MDLSTM [54]; MSA [20]; QD-DFL [55]. Our proposed approach achieves finest performance.…”

Section: Evaluation On Veri-776mentioning

confidence: 99%

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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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Section: Comparasion With Other Methodsmentioning

confidence: 99%

Section: Evaluation On Veri-776mentioning

confidence: 99%

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

et al. 2023

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Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Moral

García-Martín

Martínez

et al. 2023

Multimed Tools Appl

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Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.

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