PEVR: Pose Estimation for Vehicle Re-Identification

Tumrani, Saifullah; Deng, Zhiyi; Khan, Abdullah; Ali, Waqar

doi:10.1007/978-3-030-33982-1_6

Cited by 4 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such networks have achieved higher performance in many Artificial Intelligence (AI) applications. AI based automation has already proven itself in other domains [11][12][13].…”

Section: Proposed Methodsmentioning

confidence: 99%

H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Khan

Shafiq

Kumar

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

Self Cite

View full text Add to dashboard Cite

With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.

show abstract

“…Such networks have achieved higher performance in many Artificial Intelligence (AI) applications. AI based automation has already proven itself in other domains [11][12][13].…”

Section: Proposed Methodsmentioning

confidence: 99%

H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Khan

Shafiq

Kumar

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract