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Human activity recognition challenge

Ahad, Md Atiqur Rahman.

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Human activity recognition challenge

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Human activity recognition challenge/ edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue.
其他作者:	Ahad, Md Atiqur Rahman.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xiv, 126 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Summary of the Cooking Activity Recognition Challenge -- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN -- Chapter 3. Let's not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset -- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data -- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data -- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge -- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition -- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote -- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge -- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework.
Contained By:	Springer Nature eBook
標題:	Human activity recognition. -
電子資源:	https://doi.org/10.1007/978-981-15-8269-1
ISBN:	9789811582691

Human activity recognition challenge
Human activity recognition challenge[electronic resource] /edited by Md Atiqur Rahman Ahad, Paula Lago, Sozo Inoue. - Singapore :Springer Singapore :2021. - xiv, 126 p. :ill., digital ;24 cm. - Smart innovation, systems and technologies,v.1992190-3018 ;. - Smart innovation, systems and technologies ;v.199..

Chapter 1. Summary of the Cooking Activity Recognition Challenge -- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN -- Chapter 3. Let's not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset -- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data -- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data -- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge -- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition -- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote -- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge -- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework.

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)

ISBN: 9789811582691

Standard No.: 10.1007/978-981-15-8269-1doiSubjects--Topical Terms:

2002448
Human activity recognition.

LC Class. No.: TK7882.P7 / H86 2021

Dewey Class. No.: 006.4

Human activity recognition challenge
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505 0 $a Chapter 1. Summary of the Cooking Activity Recognition Challenge -- Chapter 2. Activity Recognition from Skeleton and Acceleration Data Using CNN and GCN -- Chapter 3. Let's not make it complicated - Using only LightGBM and Naive Bayes for macro and micro activity recognition from a small dataset -- Chapter 4. Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data -- Chapter 5. SCAR-Net: Scalable ConvNet for Activity Recognition with multi-modal Sensor Data -- Chapter 6. Multi-Sampling Classifiers for the Cooking Activity Recognition Challenge -- Chapter 7. Multi-class Multi-label Classification for Cooking Activity Recognition -- Chapter 8. Cooking Activity Recognition with Convolutional LSTM using Multi-label Loss Function and Majority Vote -- Chapter 9. Identification of Cooking Preparation Using Motion Capture Data: A Submission to the Cooking Activity Recognition Challenge -- Chapter 10. Cooking Activity Recognition with Varying Sampling Rates using Deep Convolutional GRU Framework.
520 $a 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)
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