Coral-Pie

Xu, Zhuangdi; Shah, Harshil; Ramachandran, Umakishore

doi:10.1145/3423211.3425686

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The pose keypoint regressor takes a vehicle image as input and yields 20 response maps of the vehicle's keypoints. As shown in Figure 6 and C 4 = [3,4,5,7,12,13,16,18]. The final output region masks are computed as the summation of all the feature maps belonging to each cluster:…”

Section: Keypoint-based Pose Classifiermentioning

confidence: 99%

“…In recent years, there has been an explosive growth of massive surveillance camera installations that become an indispensable part of human life in urban public spaces with particular benefits for security services [1][2][3][4][5]. One of the significant functions of urban surveillance systems is to assist security officers in locating suspicious objects whereby the task entails an input query object's image, and the objective is to search for the same target in videos recorded by various cameras.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Re-Identification with Spatio-Temporal Model Leveraging by Pose View Embedding

et al. 2022

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Vehicle re-identification (Re-ID) research has intensified as numerous advancements have been made along with the rapid development of person Re-ID. In this paper, we tackle the vehicle Re-ID problem in open scenarios. This research differs from the early-stage studies that focused on a certain view, and it faces more challenges due to view variations, illumination changes, occlusions, etc. Inspired by the research of person Re-ID, we propose leveraging pose view to enhance the discrimination performance of visual features and utilizing keypoints to improve the accuracy of pose recognition. However, the visual appearance information is still limited by the changing surroundings and extremely similar appearances of vehicles. To the best of our knowledge, few methods have been aware of the spatio-temporal information to supplement visual appearance information, but they neglect the influence of the driving direction. Considering the peculiar characteristic of vehicle movements, we observe that vehicles’ poses on camera views indicating their directions are closely related to spatio-temporal cues. Consequently, we design a two-branch framework for vehicle Re-ID, including a Keypoint-based Pose Embedding Visual (KPEV) model and a Keypoint-based Pose-Guided Spatio-Temporal (KPGST) model. These models are integrated into the framework, and the results of KPEV and KPGST are fused based on a Bayesian network. Extensive experiments performed on the VeRi-776 and VehicleID datasets related to functional urban surveillance scenarios demonstrate the competitive performance of our proposed approach.

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