Two Stream Pose Guided Network for Vehicle Re-identification

Tumrani, Saifullah; Parivish, Parivish; Khan, Abdullah; Ali, Wazir

doi:10.1145/3469951.3469954

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Vehicle re-id is related to the growth of mass transportation and urban population. With the rising significance of public security, many researchers have proposed various image-based re-id algorithms, and CNNs have been extensively used to tackle re-id tasks [1,10,10]. The advancement in CNNs have helped researcher achieve robust and discriminative data representations.…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…Multiple approaches [2,13,16,23] were proposed to deal with training problems due to intense variations in viewpoint. VAAG A few previous techniques used vehicle key-point annotations [24,24,25], while others utilized pose estimation network [1,4,26] to learn viewpoint information. Moreover, in practical applications, data annotation is incredibly expensive, and pose estimation is subject to various constraints, including motion blurriness and occlusion.…”

Section: Introductionmentioning

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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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View-aware attribute guided network for vehicle re-identification

Tumrani

Ali

Kumar

et al. 2022

Preprint

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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 predic-tor 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 B Fig. 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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Two Stream Pose Guided Network for Vehicle Re-identification

Cited by 2 publications

References 14 publications

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

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

View-aware attribute guided network for vehicle re-identification

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