Hotspots detection for machine operation in egocentric vision

Longfei Chen, Kazuaki Kondo, Yuichi Nakamura, Dima Damen, Walterio Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
291 Downloads (Pure)

Abstract

This paper introduces a novel idea of unsupervised hotspots detection from first person vision (FPV) records. The purpose is to gather typical patterns of machine operations based on touching or manipulating those hotspots and summarize the patterns as guides for operations such as online operating manuals. We chose sewing machine operation as an example and demonstrated that, a good performance of hotspots detection can be achieved by utilizing multiple features, especially touch and hand motion. More importantly, detected hotspots in both temporal and spatial locations matches well the positions of key components such as buttons, levers, and other important portions essential for operating the machine.
Original languageEnglish
Title of host publication2017 15th IAPR International Conference on Machine Vision Applications (MVA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
DOIs
Publication statusPublished - 20 Jul 2017
Event15th IAPR Conference on Machine Vision Applications : (MVA2017) -
Duration: 8 May 2017 → …

Conference

Conference15th IAPR Conference on Machine Vision Applications
Period8/05/17 → …

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  • Cite this

    Chen, L., Kondo, K., Nakamura, Y., Damen, D., & Mayol-Cuevas, W. (2017). Hotspots detection for machine operation in egocentric vision. In 2017 15th IAPR International Conference on Machine Vision Applications (MVA) Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.23919/MVA.2017.7986841