Out of sight: a toolkit for tracking occluded human joint positions

Wu, Chi‐Jui; Quigley, Aaron; Harris-Birtill, David

doi:10.1007/s00779-016-0997-6

Cited by 13 publications

(8 citation statements)

References 30 publications

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“…The results were provided as CDFs and plots based on the error distance and a few percentile values. Out of Sight [ 59 ] is a toolkit for tracking occluded human joint positions based on Kinect cameras. Some in-room test were run for evaluating different contexts (stationary, stepping, walking, presence of obstacle and oclusion).…”

Section: Diversity Problem Reviewmentioning

confidence: 99%

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Potortì

Park

Ruiz

et al. 2017

Sensors

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In recent years, indoor localization systems have been the object of significant research activity and of growing interest for their great expected social impact and their impressive business potential. Application areas include tracking and navigation, activity monitoring, personalized advertising, Active and Assisted Living (AAL), traceability, Internet of Things (IoT) networks, and Home-land Security. In spite of the numerous research advances and the great industrial interest, no canned solutions have yet been defined. The diversity and heterogeneity of applications, scenarios, sensor and user requirements, make it difficult to create uniform solutions. From that diverse reality, a main problem is derived that consists in the lack of a consensus both in terms of the metrics and the procedures used to measure the performance of the different indoor localization and navigation proposals. This paper introduces the general lines of the EvAAL benchmarking framework, which is aimed at a fair comparison of indoor positioning systems through a challenging competition under complex, realistic conditions. To evaluate the framework capabilities, we show how it was used in the 2016 Indoor Positioning and Indoor Navigation (IPIN) Competition. The 2016 IPIN competition considered three different scenario dimensions, with a variety of use cases: (1) pedestrian versus robotic navigation, (2) smartphones versus custom hardware usage and (3) real-time positioning versus off-line post-processing. A total of four competition tracks were evaluated under the same EvAAL benchmark framework in order to validate its potential to become a standard for evaluating indoor localization solutions. The experience gained during the competition and feedback from track organizers and competitors showed that the EvAAL framework is flexible enough to successfully fit the very different tracks and appears adequate to compare indoor positioning systems.

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Section: Diversity Problem Reviewmentioning

confidence: 99%

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Potortì

Park

Ruiz

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…Also, due to limited resolution of the sensor (640x480 pixels), the algorithm detected both individuals as multiple persons (e.g., when wearing high-contrast colored clothing), as well as groups of people as individuals (e.g., when moving close together), leading to false positives (FP) and false negatives (FN), as indicated in Table 1. The accuracy of the tool can be increased by deploying multiple cameras [22] or reconstructing occluded parts of the scene [28].…”

Section: Discussionmentioning

confidence: 99%

Audience monitor

Elhart

Mikusz

Mora

et al. 2017

Proceedings of the 6th ACM International Symposium on Pervasive Displays

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Understanding an audience's behavior is an important aspect of evaluating display installations. In particular, it is important to understand how people move around in the vicinity of displays, including viewer transitions from noticing a display, through approach, to final use of the display. Despite the importance of measuring viewer mobility patterns, there are still relatively few low-cost tools that can be used with research display deployments to capture detailed spatial and temporal behavior of an audience. In this paper, we present an approach to audience monitoring that uses an off-the-shelf depth sensor and open source computer vision algorithms to monitor the space in front of a digital display, tracking presence and movements of both passers-by and display users. We believe that our approach can help display researchers evaluate their public display deployments and improve the level of quantitative data underpinning our field.

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“…However, the use of the multiple vision sensors results in data fusion problems and camera calibration problems [29], [43], [44]. A Kalman filter method [40] was used to address the problem of fusion for multiple RGB-D sensors for a system that tracks a human skeleton and Wu et al [41] proposed a toolkit for tracking occluded human joints that merges the field of view of multiple RGB-D sensors and uses geometric calibration and affine transformation.…”

Section: Users' Evaluationmentioning

confidence: 99%

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

Chan

Tsai

2020

IEEE Access

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This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system. INDEX TERMS Collaborative robot, danger index, ellipsoid modeling, elite real-coded genetic algorithm (ERGA), human-robot coexistence, human-robot cooperation, RGB-D sensor, speed alteration.

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Out of sight: a toolkit for tracking occluded human joint positions

Cited by 13 publications

References 30 publications

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Comparing the Performance of Indoor Localization Systems through the EvAAL Framework

Audience monitor

Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

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