2021
DOI: 10.17586/2226-1494-2021-21-4-571-577
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A study of human motion in computer vision systems based on a skeletal model

Abstract: Methods of studying human motion in computer vision systems can be divided into two types. These are analysis in twodimensional and three-dimensional space. The former uses a single camera image and/ or multiple body sensors. Such an approach leads to a rapid accumulation of error and, consequently, low accuracy of the figure representation. Multiple cameras are usually used in the case of three-dimensional space analysis, while the objects are represented as sets of volumetric elements. Despite the high accur… Show more

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“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”
Section: Employmentmentioning
confidence: 99%
“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”
Section: Employmentmentioning
confidence: 99%