Human activity recognition challenge / Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue, editors.
Ahad, Md. Atiqur Rahman, editor.; Lago, Paula, editor.; Inoue, Sozo, editor.
2021
TK7882.P7 H86 2021eb
Available Online

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Title Human activity recognition challenge / Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue, editors.
ISBN 9789811582691 (electronic book)
9811582696 (electronic book)
9811582688
9789811582684
DOI https://doi.org/10.1007/978-981-15-8269-1
Published Singapore : Springer, [2021]
Copyright ©2021
Language English
Description 1 online resource (xiv, 126 pages)
Item Number 10.1007/978-981-15-8269-1 doi
Call Number TK7882.P7 H86 2021eb
Dewey Decimal Classification 006.4
006.3
Summary The book introduces some challenging methods and solutions to solve the human activity recognition challenge. This book highlights the challenge that will lead the researchers in academia and industry to move further related to human activity recognition and behavior analysis, concentrating on cooking challenge. Current activity recognition systems focus on recognizing either the complex label (macro-activity) or the small steps (micro-activities) but their combined recognition is critical for analysis like the challenge proposed in this book. It has 10 chapters from 13 institutes and 8 countries (Japan, USA, Switzerland, France, Slovenia, China, Bangladesh, and Columbia).
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from digital title page (viewed on February 10, 2021).
Added Author Ahad, Md. Atiqur Rahman, editor.
Lago, Paula, editor.
Inoue, Sozo, editor.
Series Smart innovation, systems, and technologies ; 199.
Available in Other Form Human Activity Recognition Challenge.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Summary of the Cooking Activity Recognition Challenge
Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN
Let's Not Make It Complicated-Using Only LightGBM and Naive Bayes for Macro- and Micro-Activity Recognition from a Small Dataset
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data
SCAR-Net: Scalable ConvNet for Activity Recognition with Multimodal Sensor Data
Multi-sampling Classifiers for the Cooking Activity Recognition Challenge
Multi-class Multi-label Classification for Cooking Activity Recognition
Cooking Activity Recognition with Convolutional LSTM Using Multi-label Loss Function and Majority Vote
Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge
Cooking Activity Recognition with Varying Sampling Rates Using Deep Convolutional GRU Framework.

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