Kinect-Based Moving Human Tracking System with Obstacle Avoidance

Ahmad, Abdel Mehsen; Bazzal, Zouhair; Youssef, Hiba Al

doi:10.25046/aj020325

Cited by 2 publications

(3 citation statements)

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“…The rest runs in real time. In terms of computer resources, a computer with advanced hardware is used in [ 76 ], as well as robotic platforms such as MOBOT [ 77 ], Hobbit [ 80 ], Roomba [ 81 ], and a customized robot [ 78 ].…”

Section: Discussion and Conclusionmentioning

confidence: 99%

“…The authors used an RGB-D camera for body pose estimation and a LIDAR sensor to detect and avoid obstacles. Nevertheless, Ahmad et al in [ 78 ] presented an algorithm for a mobile robot to follow a person around a room with obstacles. The authors used a Kinect sensor to detect the human body, utilizing a skeleton to determine the center of mass.…”

Section: Algorithms Used For the Bodymentioning

confidence: 99%

See 1 more Smart Citation

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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Section: Discussion and Conclusionmentioning

confidence: 99%

Section: Algorithms Used For the Bodymentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, a social robot detects humans using a Kinect sensor that combines colour and depth information [3], [4]. Skeleton-based techniques for recognising person pose and some activities or behaviours have been developed in conjunction with RGB-Depth sensors [5]. This task has also been addressed using stereo vision.…”

mentioning

confidence: 99%

Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

Zainudin

Kodagoda

2023

IJRCS

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Consistency of tracking filter such as Interactive Multiple Model Probabilistic Data Association Filter (IMMPDAF) is the most important factor in targets tracking. Inaccurate tracking capability will lead to poor tracking performance when dealing with multiple people's interactions and occlusions. In order to validate the consistency, Normalized Estimation Error Squared (NEES) and Normalized Innovation Squared (NIS) were evaluated and tested using Monte Carlo experiments for 50 runs. These simulations has proven that the tracker is conditionally consistent on targets tracking despite the fact that it has difficulties on handling occlusions and maneuvering people. NEES requires ground truth of tracking data and predicted data, whereas NIS requires observation and predicted data for Monte Carlo simulations. In NEES simulations, the result emphasizes that state estimation errors of IMMPDAF tracker are inconsistent with filter-calculated covariances especially when dealing with sudden turns in zig-zag motion where quite a large number of points fall outside 95\% probability region. In NIS simulations, IMMPDAF tracker is confirmed to have difficulties to handle multiple targets with a short period of occlusion although a small number of points falls outside of 95\% probability region. Filter tracker is considered mismatched when dealing with zig-zag motion; however, it deemed to be optimistic when dealing with occlusions. As a result, the IMMPDAF tracker has limited capability in monitoring sharp turns under occlusion conditions, although it is acceptable when dealing with occlusions only.

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Kinect-Based Moving Human Tracking System with Obstacle Avoidance

Cited by 2 publications

References 5 publications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Monte Carlo Simulations on 2D LRF Based People Tracking using Interactive Multiple Model Probabilistic Data Association Filter Tracker

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