| --- |
| license: cc-by-4.0 |
| --- |
| |
| A simple way to download the dataset: |
| ``` |
| # Make sure hf CLI is installed: pip install -U "huggingface_hub[cli]" |
| hf download thanhhff/MultiSensor-Home2 --repo-type=dataset --local-dir dataset/home2 |
| ``` |
|
|
| The **MultiSensor-Home1** dataset is available at: https://huggingface.co/datasets/thanhhff/MultiSensor-Home1/ |
|
|
| # MultiSensor-Home2: Benchmark for Multi-modal Multi-view Action Recognition in Home Environments |
|
|
| MultiSensor-Home2 is an extended version of MultiSensor-Home1, captured in a different home layout while maintaining the same structure and recording settings. |
| This dataset is designed for multi-view action recognition and transformer-based sensor fusion research. |
|
|
|
|
| ## 📊 Dataset Overview |
|
|
| MultiSensor-Home is a comprehensive multi-view action recognition dataset captured in a real home environment. |
| The dataset features: |
|
|
| - **Multi-view Setup**: 5 synchronized camera views (View1-View5) |
| - **High-resolution**: Original resolution 4000×3000 pixels (available upon request) |
| - **Optimized for Deep Learning**: Resized to 320×240 pixels for efficient training |
| - **Temporal Annotations**: Precise start/end timestamps for each action |
| - **Real-world Scenarios**: Natural human activities in home environment |
| - **Action Classes**: 16 different action classes in this environment |
|
|
| **Note**: The original high-resolution dataset (4000×3000 pixels) is available upon request. Please contact: nguyent [at] cs.is.i.nagoya-u.ac.jp |
|
|
| ## 🏠 Room Layout and Camera Setup |
|
|
|  |
|
|
| *Home1 floor plan showing camera positions and room layout* |
|
|
| - **Room Layout**: Complete floor plan of the home environment |
| - **Camera Positions**: Exact placement of all 5 cameras (View1-View5) |
| - **Camera Orientations**: Direction and field of view for each camera |
| - **Room Dimensions**: Spatial measurements and room configurations |
| - **Recording Environment**: Overview of the home setup used for data collection |
|
|
| This layout file is essential for understanding the spatial relationships between different camera views and the overall recording environment. |
|
|
|
|
| ## 🏠 Dataset Structure |
|
|
| ``` |
| MultiSensor-Home2/ |
| ├── 01/ # Recording session 1 |
| ├── 02/ # Recording session 2 |
| ├── 03/ # Recording session 3 |
| ├── 04/ # Recording session 4 |
| ├── 05/ # Recording session 5 |
| ├── 06/ # Recording session 6 |
| ├── 07/ # Recording session 7 |
| ├── 08/ # Recording session 8 |
| ├── 09/ # Recording session 9 |
| ├── all_labels.json # Complete annotations |
| ├── train_data.json # Training split annotations |
| ├── test_data.json # Test split annotations |
| └── README.md # This file |
| ``` |
|
|
| ## 📹 Video File Naming Convention |
|
|
| Videos follow the pattern: `{id}-{View}{number}-Part{part}.mp4` |
|
|
| **Examples:** |
| - `00-View1-Part1.mp4` - ID 00, View 1, Part 1 |
| - `15-View3-Part2.mp4` - ID 15, View 3, Part 2 |
| - `23-View5-Part1.mp4` - ID 23, View 5, Part 1 |
|
|
| ## 🏷️ Action Classes |
|
|
| The dataset contains **16 action classes** covering various human activities in the home environment: |
|
|
| - **Basic Movements**: Sitdown, Standup, Enter, Exit |
| - **Device Usage**: UseLaptop, UsePhone, ReadBook |
| - **Environmental Control**: TurnOnLamp, TurnOffLamp |
| - **Home Activities**: OpenCurtain, CloseCurtain, Eat, Drink |
| - **And more...** |
|
|
| ## 📋 Annotation Format |
|
|
| Each video segment is annotated with: |
|
|
| ```json |
| { |
| "video_url_1": "01/00-View1-Part1.mp4", |
| "video_url_2": "01/00-View2-Part1.mp4", |
| "video_url_3": "01/00-View3-Part1.mp4", |
| "video_url_4": "01/00-View4-Part1.mp4", |
| "video_url_5": "01/00-View5-Part1.mp4", |
| "tricks": [ |
| { |
| "start": 0.6758380883417825, |
| "end": 6.314058810132165, |
| "channel": 0, |
| "labels": [ |
| "Enter" |
| ] |
| } |
| } |
| ``` |
|
|
| ### Annotation Fields: |
| - **video_url_1-5**: Paths to the 5 synchronized video views |
| - **start/end**: Temporal boundaries in seconds |
| - **labels**: Action label for the time segment |
|
|
|
|
| ## 📧 Original High-Resolution Dataset |
|
|
| The original dataset at full resolution (4000×3000 pixels) is available upon request. |
|
|
| Please include: |
| - Your name and affiliation |
| - Intended use of the dataset |
| - Brief description of your research |
|
|
| ## 📄 License and Citation |
|
|
| When using this dataset, please cite our paper: |
|
|
| ```bibtex |
| @inproceedings{nguyen2025multisensor, |
| author = {Trung Thanh Nguyen and Yasutomo Kawanishi and Vijay John and Takahiro Komamizu and Ichiro Ide}, |
| title = {MultiSensor-Home: A Wide-area Multi-modal Multi-view Dataset for Action Recognition and Transformer-based Sensor Fusion}, |
| booktitle = {Proceedings of the 19th IEEE International Conference on Automatic Face and Gesture Recognition}, |
| year = {2025}, |
| note = {Best Student Paper Award} |
| } |
| ``` |
|
|
| ## 🤝 Contributing |
|
|
| We welcome contributions and feedback. If you find any issues or have suggestions for improvements, please contact us. |
|
|
| ## 📞 Contact |
|
|
| For questions about the dataset, paper, or to request the original high-resolution version: |
|
|
| **Email**: nguyent [at] cs.is.i.nagoya-u.ac.jp |
|
|
| ## Acknowledgement |
| This work was partly supported by Japan Society for the Promotion of Science (JSPS) KAKENHI JP21H03519 and JP24H00733. |
|
|
| --- |
|
|
| *This dataset is designed to advance research in multi-view action recognition, sensor fusion, and transformer-based approaches for understanding human activities in real-world environments.* |
|
|
