Multi Activity Recognition Based on Bodymodel-Derived Primitives 1st edition by Andreas Zinnen, Christian Wojek, Bernt Schiele ISBN 3642017209 9783642017209
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Authors:Andreas Zinnen, Christian Wojek; Bernt Schiele , Tags:Location and Context Awareness , Author sort:Andreas Zinnen, Christian Wojek & Schiele, Bernt , Languages:Languages:eng , Published:Published:Jan 2010 , Comments:Comments:Location and Context Awareness
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Product details:
ISBN 10: 3642017209
ISBN 13: 978-3642017209
Author: Andreas Zinnen, Christian Wojek, Bernt Schiele
We propose a novel model-based approach to activity recognition using high-level primitives that are derived from a human body model estimated from sensor data. Using short but fixed positions of the hands and turning points of hand movements, a continuous data stream is segmented in short segments of interest. Within these segments, joint boosting enables the automatic discovery of important and distinctive features ranging from motion over posture to location. To demonstrate the feasibility of our approach we present the user-dependent and across-user results of a study with 8 participants. The specific scenario that we study is composed of 20 activities in quality inspection of a car production process.
Multi Activity Recognition Based on Bodymodel-Derived Primitives 1st Table of contents:
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Introduction
- Overview of Activity Recognition
- Motivation and Significance of Using Bodymodel-Derived Primitives
- Objectives and Scope of the Study
- Structure of the Paper
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Related Work
- Review of Existing Activity Recognition Approaches
- Challenges in Multi-Activity Recognition
- Use of Body Models in Activity Recognition
- Previous Approaches Utilizing Primitives in Activity Recognition
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Methodology
- Overview of the Proposed Approach
- Description of Bodymodel-Derived Primitives
- Definition and Extraction of Primitives
- Integration of Body Models
- Multi-Activity Recognition Framework
- Data Collection and Preprocessing
- Feature Extraction
- Machine Learning and Classification Methods
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Experimental Setup
- Description of Datasets
- Evaluation Metrics
- Experimental Design
- Implementation Details
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Results
- Performance Analysis on Different Datasets
- Comparison with Existing Methods
- Effect of Bodymodel-Derived Primitives on Recognition Accuracy
- Analysis of Computational Efficiency
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Discussion
- Key Insights and Implications of Results
- Limitations and Challenges
- Potential Improvements to the Approach
- Real-World Applications of Multi Activity Recognition
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Conclusion
- Summary of Key Findings
- Contributions to the Field
- Future Work and Directions
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References
- List of Cited Works
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Appendices (if applicable)
- Additional Experimental Data
- Code or Algorithmic Details
- Supplementary Information
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