Tracking persons using a network of RGBD cameras

Galanakis, George; Zabulis, Xenophon; Koutlemanis, Panagiotis; Paparoulis, Spiros; Kouroumalis, Vassilis

doi:10.1145/2674396.2674467

Cited by 7 publications

(23 citation statements)

References 27 publications

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“…Torso and legs colours are then used to build the appearance model, by concatenating histograms of colour and texture for both regions. Galamakis et al [49] also exploit depth to produce a two‐part colour histogram model of upper and lower body, using their textured mesh representation obtained during their ROI selection stage. The mesh is divided into lower and upper body parts at an empirically determined height.…”

Section: Survey By Use Of Depth Informationmentioning

confidence: 99%

“…The methods in [44, 73] operate on a top‐down view due to their specific detection strategy centred around monitoring humans seen from above. In [49], a sufficient coverage of the scene by multiple cameras at various viewing angles is preferred to produce 3D textured meshes of humans. In [62], the camera is placed on the ceiling at an angle of

30^{\circ}

, so as to reduce occlusions as upper body parts are always visible.…”

Section: Considerations On the Practical Applications Of Mhtmentioning

confidence: 99%

“…Dynamicsliding window Dynamicfull history Bansal et al [38] (1 frame) Salas & Tomasi [39] (1 frame) Dan et al [40] (1 frame) Darrell et al [41] Bajracharya et al [43] (1 frame) Zhang et al [44] Galanakis et al [45] Liu et al [46,47] ? ?…”

Section: Staticmentioning

confidence: 99%

“…Methods, such as those in [43, 45-47, 53, 56, 57, 63, 64], project detections onto a flat ground plane. In [43,45,56,57], ROIs are not selected based on height from this plane 1 , so the flat ground assumption does not need to correspond to reality. However, this is not the case in [46,47,53,63,64] where people have to be located in a relatively narrow band above the floor to be detected.…”

Section: Specific Constraintsmentioning

confidence: 99%

“…Depth map denoising and completion -Zhang et al [44] suppress outliers from the 3D point clouds by removing the points that have only a few neighbors. In Galanakis et al [45], the overlapping views of the structured light sensors create interferences that add noise to the scene. The authors report that in their setup this noise is predominantly on the ground plane and negligible on humans, and use an estimate of the ground plane to eliminate any points close to the ground in their ROI selection stage.…”

Section: Preprocessing the Depth Mapmentioning

confidence: 99%

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Multiple human tracking in RGB‐depth data: a survey

Camplani

Paiement

Mirmehdi

et al. 2017

IET Computer Vision

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Multiple human tracking (MHT) is a fundamental task in many computer vision applications. Appearance-based approaches, primarily formulated on RGB data, are constrained and affected by problems arising from occlusions and/or illumination variations. In recent years, the arrival of cheap RGB-Depth (RGB-D) devices has led to many new approaches to MHT, and many of these integrate color and depth cues to improve each and every stage of the process. In this survey, we present the common processing pipeline of these methods and review their methodology based (a) on how they implement this pipeline and (b) on what role depth plays within each stage of it. We identify and introduce existing, publicly available, benchmark datasets and software resources that fuse color and depth data for MHT. Finally, we present a brief comparative evaluation of the performance of those works that have applied their methods to these datasets.

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Section: Survey By Use Of Depth Informationmentioning

confidence: 99%