Depth Guided Attention for Person Re-identification

Uddin, Kamal; Lam, Antony; Fukuda, Hisato; Kobayashi, Yoshinori; Kuno, Yoshinori

doi:10.1007/978-3-030-60796-8_10

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Other ReID techniques use both raw RGB and depth data [8,15,16]. Additionally, depth images are sometimes utilized as body segmentation masks, which are useful in removing cluttered backgrounds from scenes, as in the approaches proposed by [11,17]. The majority of ReID methods that rely on RGB-depth images are based on deep learning and employ score-level [18] or featurelevel fusion [19] techniques.…”

Section: Reid Using Rgb and Depth Datamentioning

confidence: 99%

Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Bilakeri,

Kotegar

2024

Eng. Res. Express

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Noisy features may introduce irrelevant or incorrect features that can lead to incorrect classifications and lower accuracy. This can be especially problematic in tasks such as person re-identification(ReID), where subtle differences between individuals need to be accurately captured and distinguished. However, the existing ReID methods directly use noisy and limited multimodality featuresfor similarity measures. It is crucial to use robust features and pre-processing techniques to reducethe effects of noise and ensure accurate classification. As a solution, we employ a Gaussian filter toeliminate the Gaussian noise from RGB-D data in the pre-processing stage. For similarity measure,the color descriptors are computed using the top eight peaks of the 2D histogram constructed frompose regularized partition grid cells, and eleven different skeleton distances are considered. The proposed method is evaluated on the BIWI RGBD-ID dataset, which comprises still (front view images)and walking set (images with varied pose and viewpoint) images. The obtained recognition ratesof 99.15% and 94% on still and walking set images demonstrate the effectiveness of the proposedapproach for the ReID task in the presence of pose and viewpoint variations. Further, the methodis evaluated on and RGBD-ID and achieved improved performance over the existing techniques.

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Section: Reid Using Rgb and Depth Datamentioning

confidence: 99%

Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Bilakeri,

Kotegar

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Apart from the above works, some Re-ID methods use raw-depth and RGB images combinedly [ 40 , 42 , 47 ]. Moreover, in some approaches [ 48 , 57 ], depth frames are used as body segmentation masks, which play an important role in removing cluttered backgrounds from scenes. Most of the above RGB-depth image-based Re-ID methods are considered deep-learning approaches with score-level or feature-level fusion techniques.…”

Section: Multi-modal Person Re-identificationmentioning

confidence: 99%

“…In the Re-ID literature, publicly available RGB–D and IR sensor-based datasets [ 4 , 14 , 33 , 35 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] have been recorded for specific purposes because some Re-ID approaches use different domains combinedly to improve the performance of Re-ID [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 47 , 48 ] while other approaches use cross-domain methods [ 49 , 50 , 51 , 52 , 53 ] in the Re-ID framework to continue surveillance between daytime visible images and nighttime infrared images. Still, some Re-ID methods consider only IR–IR [ 17 , 54 ] matching using the publicly available datasets.…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Uddin

Bhuiyan

Bappee

et al. 2023

Sensors

Self Cite

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Learning about appearance embedding is of great importance for a variety of different computer-vision applications, which has prompted a surge in person re-identification (Re-ID) papers. The aim of these papers has been to identify an individual over a set of non-overlapping cameras. Despite recent advances in RGB–RGB Re-ID approaches with deep-learning architectures, the approach fails to consistently work well when there are low resolutions in dark conditions. The introduction of different sensors (i.e., RGB–D and infrared (IR)) enables the capture of appearances even in dark conditions. Recently, a lot of research has been dedicated to addressing the issue of finding appearance embedding in dark conditions using different advanced camera sensors. In this paper, we give a comprehensive overview of existing Re-ID approaches that utilize the additional information from different sensor-based methods to address the constraints faced by RGB camera-based person Re-ID systems. Although there are a number of survey papers that consider either the RGB–RGB or Visible-IR scenarios, there are none that consider both RGB–D and RGB–IR. In this paper, we present a detailed taxonomy of the existing approaches along with the existing RGB–D and RGB–IR person Re-ID datasets. Then, we summarize the performance of state-of-the-art methods on several representative RGB–D and RGB–IR datasets. Finally, future directions and current issues are considered for improving the different sensor-based person Re-ID systems.

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Uncertainty-Guided Pixel-Level Contrastive Learning for Biomarker Segmentation in OCT Images

Bai

Liu

et al. 2021

Intelligent Computing Theories and Application

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Depth Guided Attention for Person Re-identification

Cited by 4 publications

References 21 publications

Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features

Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey

Uncertainty-Guided Pixel-Level Contrastive Learning for Biomarker Segmentation in OCT Images

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