The human motion database: A cognitive and parametric sampling of human motion

Guerra-Filho, Gutemberg; Biswas, Arnab

doi:10.1016/j.imavis.2011.12.002

Cited by 34 publications

(3 citation statements)

References 7 publications

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“…However, these datasets only contain videos since they focus on robust approaches in natural and unconstrained videos. There also exist motion capture datasets containing human interactions such as The CMU Graphics Lab Motion Capture Database (http://mocap.cs.cmu.edu/) and Human Motion Database (HMD) [9]. However, both datasets have only captured one couple (=two-actor set) so that they are not well suited for evaluating human interaction recognition performance.…”

Section: Related Workmentioning

confidence: 99%

Two-person interaction detection using body-pose features and multiple instance learning

Yun

Honorio

Chattopadhyay

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

466

470

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Human activity recognition has potential to impact a wide range of applications from surveillance to human computer interfaces to content based video retrieval. Recently, the rapid development of inexpensive depth sensors (e.g. Microsoft Kinect) provides adequate accuracy for real-time full-body human tracking for activity recognition applications. In this paper, we create a complex human activity dataset depicting two person interactions, including synchronized video, depth and motion capture data. Moreover, we use our dataset to evaluate various features typically used for indexing and retrieval of motion capture data, in the context of real-time detection of interaction activities via Support Vector Machines (SVMs). Experimentally, we find that the geometric relational features based on distance between all pairs of joints outperforms other feature choices. For whole sequence classification, we also explore techniques related to Multiple Instance Learning (MIL) in which the sequence is represented by a bag of body-pose features. We find that the MIL based classifier outperforms SVMs when the sequences extend temporally around the interaction of interest.

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Section: Related Workmentioning

confidence: 99%

Two-person interaction detection using body-pose features and multiple instance learning

Yun

Honorio

Chattopadhyay

et al. 2012

2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

466

470

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“…For example, during the COVID-19 pandemic, remotely controlled robots can be used for the construction of hospitalization facilities [86]. Extending motion capturing, human motion prediction that estimates human's movement in a short period with historical movements or certain leading indicators [87] has the significant application potential in HRC. To this end, Guerra-Filho and Biswas [88] developed a dataset for human motion prediction with additional cognitive features.…”

Section: Human-sensing Technologies In Constructionmentioning

confidence: 99%

Intelligent robots and human–robot collaboration in the construction industry: A review

Wei¹,

Zhang²,

Sun³

et al. 2023

Journal of Intelligent Construction

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Keywords human-robot collaboration (HRC) robotics innovation adaption taxonomyThe construction industry is a typical labor-intensive industry, which suffers from low productivity and labor shortage in the past decades. Recently, the developments in robotics and artificial intelligence technologies highlight the evolutionary reforming potential in the construction industry. An increasing number of robots are joining construction tasks and collaborating with human workers. This study reviews the major developments in intelligent robots and human-robot collaboration (HRC) in the construction industry. The technological foundations and fundamental concepts of construction robots and collaborative robots are reviewed, organized, and discussed to reveal that progress has been made. Based on a comprehensive review, the major challenges and future research directions of HRC have been proposed and examined. This study finally developed a comprehensive and in-depth discussion of the state-ofthe-art implementation of robotics technologies in the construction industry and shed light on its path to future development.

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“…The first available datasets were focusing on full-body movements to improve motion capture [14], pose estimation [27] or activity recognition [16][22] [20] using expansive synchronized motion capture systems and video cameras. Datasets of sign language have also been frequent topics of research [21] [8], mostly based on webcams to record hands or upper body of subjects.…”

Section: State Of the Artmentioning

confidence: 99%

ChAirGest

Ruffieux

Lalanne

Mugellini

2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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In this paper, we present a research oriented open challenge focusing on multimodal gesture spotting and recognition from continuous sequences in the context of close human-computer interaction. We contextually outline the added value of the proposed challenge by presenting most recent and popular challenges and corpora available in the field. Then we present the procedures for data collection, corpus creation and the tools that have been developed for participants. Finally we introduce a novel single performance metric that has been developed to quantitatively evaluate the spotting and recognition task with multiple sensors.

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The human motion database: A cognitive and parametric sampling of human motion

Cited by 34 publications

References 7 publications

Two-person interaction detection using body-pose features and multiple instance learning

Two-person interaction detection using body-pose features and multiple instance learning

Intelligent robots and human–robot collaboration in the construction industry: A review

ChAirGest

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