Kinects and human kinetics: A new approach for studying pedestrian behavior

Seer, Stefan; Brändle, Norbert; Ratti, Carlo

doi:10.1016/j.trc.2014.08.012

Cited by 100 publications

(87 citation statements)

References 45 publications

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“…To collect pedestrian trajectories, following [13], we developed a system with the following characteristics. Via a commercial low-cost Microsoft Kinect 3D range sensor [12] we collect raw overhead depth maps of the corridor (sensor height, 4 m; time resolution, 15 frames per second).…”

Section: Measurement Setupmentioning

confidence: 99%

“…We process the depth-map stream off-line, extracting the head positions frame by frame (cf. [13]); thus, we perform the tracking in a particle tracking velocimetry (PTV) fashion [14] via the library OpenPTV [15]. Through this procedure, further described in Appendixes A-C, we achieve a typical detection and tracking error within a centimeter.…”

Section: Measurement Setupmentioning

confidence: 99%

“…Clusters are found after cutting the hierarchical clustering dendrogram at a height commensurable with the shoulder size (cf. reliability analysis in [13]). Finally, heads are associated with the "upper" part (i.e., having lesser depth) of each cluster (fifth percentile).…”

Section: Appendix A: Depth Maps Acquisition and Pedestrian Trackingmentioning

confidence: 99%

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Fluctuations around mean walking behaviors in diluted pedestrian flows

et al. 2017

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Understanding and modeling the dynamics of pedestrian crowds can help with designing and increasing the safety of civil facilities. A key feature of crowds is its intrinsic stochasticity, appearing even under very diluted conditions, due to the variability in individual behaviours. Individual stochasticity becomes even more important under densely crowded conditions, since it can be nonlinearly magnified and may lead to potentially dangerous collective behaviours. To understand quantitatively crowd stochasticity, we study the real-life dynamics of a large ensemble of pedestrians walking undisturbed, and we perform a statistical analysis of the fully-resolved pedestrian trajectories obtained by a year-long high-resolution measurement campaign. Our measurements have been carried out in a corridor of the Eindhoven University of Technology via a combination of Microsoft Kinect TM 3D-range sensor and automatic headtracking algorithms. The temporal homogeneity of our large database of trajectories allows us to robustly define and separate average walking behaviours from fluctuations parallel and orthogonal with respect to the average walking path. Fluctuations include rare events when individuals suddenly change their minds and invert their walking direction. Such tendency to invert direction has been poorly studied so far even if it may have important implications on the functioning and safety of facilities. We propose a novel model for the dynamics of undisturbed pedestrians, based on stochastic differential equations, that provides a good agreement with our experimental observations, including the occurrence of rare events.

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Section: Measurement Setupmentioning

confidence: 99%

Section: Measurement Setupmentioning

confidence: 99%

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Fluctuations around mean walking behaviors in diluted pedestrian flows

et al. 2017

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“…Automated human body tracking is the ability to identify and follow individuals in an environment, usually through human pose estimation and spatial recognition software. Inexpensive depth-sensing technologies, such as the timeof-flight camera within the Microsoft Kinect, have enabled the human body to be segmented, and subsequently tracked, in systems such as pedestrian behavior analysis [1], human-robot interactions [2], gait recognition [3], and cross-device interactions [4]. It's common for researchers and developers to leverage such noninvasive tracking infrastructure (e.g., through the Microsoft Kinect Software Development Kit 1 ) to support gesture control and novel forms of human-computer interaction (HCI).…”

Section: Introductionmentioning

confidence: 99%

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

Quigley

Harris-Birtill

2016

Pers Ubiquit Comput

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Real-time identification and tracking of the joint positions of people can be achieved with off-the-shelf sensing technologies such as the Microsoft Kinect, or other camera-based systems with computer vision. However, tracking is constrained by the system's field of view of people. When a person is occluded from the camera view, their position can no longer be followed. Out of Sight addresses the occlusion problem in depth-sensing tracking systems. Our new tracking infrastructure provides human skeleton joint positions during occlusion, by combining the field of view of multiple Kinects using geometric calibration and affine transformation. We verified the technique's accuracy through a system evaluation consisting of 20 participants in stationary position and in motion, with two Kinects positioned parallel, 45, and 90 apart. Results show that our skeleton matching is accurate to within 16.1 cm (s.d. = 5.8 cm), which is within a person's personal space. In a realistic scenario study, groups of two people quickly occlude each other, and occlusion is resolved for 85% of the participants. A RESTful API was developed to allow distributed access of occlusion-free skeleton joint positions. As a further contribution, we provide the system as open source.

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“…Some researchers have recently shown interest in 3D image sensors such as Kinect. A method for collecting trajectories with multiple Kinect sensors has been developed by Stefan Seera [1].…”

Section: Introductionmentioning

confidence: 99%

Construction Progress Management and Interior Work Analysis Using Kinect 3D Image Sensors

Ishida¹

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Construction progress management and work analysis are often difficult in building interiors. In order to make progress management easier, the authors developed methods for recording the interior work environment. These methods record data using Microsoft Kinect sensors.In this paper, the authors describe methods for recording work data utilizing several Kinect sensors. The authors recorded the following types of data: 1. The work environment at the construction site 2. The shape of the building at the construction site 3. The work efficiency at the site derived from the motion capture data To record the three types of data, the authors developed a method for performing motion capture using several Kinects.In addition, a shape recognition method for identifying building materials was developed. The shape recognition method utilizes point cloud data and camera image data. The authors also studied methods for analyzing the work efficiency data on the basis of skeletal tracking information.

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Kinects and human kinetics: A new approach for studying pedestrian behavior

Cited by 100 publications

References 45 publications

Fluctuations around mean walking behaviors in diluted pedestrian flows

Fluctuations around mean walking behaviors in diluted pedestrian flows

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

Construction Progress Management and Interior Work Analysis Using Kinect 3D Image Sensors

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