Bento Packaging Activity Recognition Challenge

- Submission Dates:
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to
- Citation Author(s):
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Kohei Adachi (Kyushu Institute of Technology)Nazmun Nahid (Kyushu Institute of Technology)Sozo Inoue (Kyushu Institute of Technology)
- Submitted by:
- Sayeda Shamma Alia
- Last updated:
- DOI:
- 10.21227/cwhs-t440
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Abstract
Human activity recognition (HAR) has been one of the most prevailing and persuasive research topics in different fields for the past few decades. The main idea is to comprehend individuals’ regular activities by looking at bits of knowledge accumulated from people and their encompassing living environments based on sensor observations. HAR has a great impact on human-robot collaborative work, especially in industrial works. In compliance with this idea, we have organized this year’s Bento Packaging Activity Recognition Challenge. In this challenge, participants will have to design methods to identify different activities done during Bento-box packaging from MOCAP data. The data has been collected during the Bento-box packaging sessions inside the lab. We will organize a workshop at ABC Conference to discuss the pros and cons of the different approaches shared by the participants of the challenge and future perspectives of this topic.
Instructions:
The Bento Packaging Activity Recognition Challenge is organized by Sozolab.
RegistrationParticipants should accept all the terms and conditions in this page before registering. By registering, participants agree to these terms. Participants should also register as workshop participants in ABC. Participants not registered in the conference will not be considered in the final leaderboard of the challenge nor for the Prizes.
ElegibilityChallenge is open to students, graduate students, researchers, professors, and data scientists. Members of Sozolab (“Organizers”) are not eligible to enter or win. Only submissions with a submitted paper are eligible to win a prize.
Workshop ParticipationAll participants are encouraged to participate in the Workshop to share details about their approach.
Data useAll participants may use the data free of charge under CC license.
Team LimitsThe maximum size of a team is 10 participants.