Re-identification with RGB-D Sensors

By Igor Barros Barbosa, Marco Cristani, Alessio Del Bue, Loris Bazzani, Vittorio Murino. 2012

215 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “4 RGB-D person re-identification dataset RGB-D Person Re-identification dataset [5] was jointly created in 2012 by Italian Institute of Technology, University of Verona and University of Burgundy, aiming to promote the research of the RGB-D person re-identification.”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Many other datasets captured using a Kinect v1 camera were also released to the public, including the MSR Daily Activity 3D [121], MSR Action Pairs [113], Online RGBD Action (ORGBD) [107], UTKinect-Action [124], Florence 3D-Action [118], CMU-MAD [104], UTD-MHAD [103], G3D/G3Di [105], [119], SPHERE [”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
One-Shot Person Re-identification with a Consumer Depth Camera (2014, methodology): “From this perspective, only a dataset has been proposed so far [2].”
End-to-End Deep Learning for Person Search (2016, background): “Many person reidentification datasets have emerged in recent years, including VIPeR [9], ETHZ [29], i-LIDS [39], PRID2011 [13], RGB-D [2], GRID [22], CUHK01 [17], CUHK02 [16], and Multi-Camera Surveillance Database [3].”
Deep Learning for Person Re-Identification: A Survey and Outlook (2020, methodology): “Some methods [53], [185] have also studied the combination of RGB and depth information to improve the Re-ID performance, addressing the clothes-changing challenge.”
Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-identification (2017, methodology): “Next, we report results from [6,59], who use hand-crafted features based on biometrics, such as distances between skeleton joints.”
Deep Multimodal Data Fusion (2024, unknown): “…Face Anti Proofing RGB + Thermal CASIA-SURF [230], CASIA-SURF Cefa [102] Speakingfaces [1], ThermalFace [8], TTVF [63], UL-FMTV [52], Person Re-Identification RGB+Thermal RegDB [131], SYSU-MM01 [196], BIWI RGBD-ID [127], RGBD-ID [13], SUCVL RGBD-ID [120], RobotPKU [105], KinectREID [135] task.”
RGBD Datasets: Past, Present and Future (2016, background): “RGB-D Person Reidentification [8] 79 Humans walking, where subjects change clothes between sessions 2D skeleton positions.”
Person Re-identification Using the Silhouette Shape Described by a Point Distribution Model (2015, methodology): “We use the RGB-ID [2] to evaluate the shape descriptor.”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, methodology): “In [10] such joints were used to extract a set of different anthropometric measures from front or back poses: distance between floor and head, ratio between torso and legs, height, distance between floor and neck, distance between neck and left shoulder, distance between neck and right shoulder, and”
Recurrent Attention Models for Depth-Based Person Identification (2016, methodology): “For PAVIS, we use Walking1 and Walking2 as the training and test set, respectively.”
Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification (2020, background): “In particular, they try to integrate the visible and thermal domains [36] or RGB and depth information [6], [37].”
Person Recognition in Personal Photo Collections (2017, background): “Datasets include RGBD-ID [41], IAS-Lab RGBD-ID [42], and”
Multi-modality imagery database for plant phenotyping (2016, methodology): “The depth measurement has been a component of a number of recent non-plant RGB-D databases designed for object recognition [Lai et al., 2011], scene segmentation [Silberman & Fergus, 2011], human analysis [Sung et al., 2011,Barbosa et al., 2012], and mapping [Sturm et al., 2012].”
The role of RGB-D benchmark datasets: an overview (2013, background): “They provide seven different scenes including RGB, depth and pose data in a txt-file.”
A survey of approaches and trends in person re-identification (2014, unknown): “In order to boost Re-ID performance, different sensors like RGB-D [112,106] and infrared [113] that capture soft biometric cues insensitive to appearance variations are being explored.”