Metadata-Version: 2.1
Name: mpose
Version: 1.0.11
Summary: MPOSE2021: a Dataset for Short-time Pose-based Human Action Recognition
Home-page: UNKNOWN
Author: Simone Angarano
License: MIT
Description: # MPOSE2021
        #### A Dataset for Real-Time Short-Time HAR
        
        This repository contains the MPOSE2021 Dataset for short-time pose-based Human Action Recognition (HAR). 
        MPOSE2021 is specifically designed to perform short-time Human Action Recognition.
        
        MPOSE2021 is developed as an evolution of the MPOSE Dataset [1-3]. It is made by human pose data detected by 
        [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) [4] and [Posenet](https://github.com/tensorflow/tfjs-models/tree/master/posenet) on popular datasets for HAR, i.e. Weizmann [5], i3DPost [6], IXMAS [7], KTH [8], UTKinetic-Action3D (RGB only) [9] and UTD-MHAD (RGB only) [10], alongside original video datasets, i.e. ISLD and ISLD-Additional-Sequences [1].
        Since these datasets have heterogenous action labels, each dataset labels are remapped to a common and homogeneous list of actions.
        
        This repository allows users to generate pose data for MPOSE2021 in a python-friendly format. 
        Generated sequences have a number of frames between 20 and 30. 
        Sequences are obtained by cutting the so-called Precursor VIDEOS (videos from the above-mentioned datasets), with non-overlapping sliding windows.
        Frames where OpenPose cannot detect any subject are automatically discarded. Resulting samples contain one subject at the time, performing a fraction of a single action. Overall, MPOSE2021 contains 15429 samples, divided into 20 actions, performed by 100 subjects. 
        
        The overview of the action composition of MPOSE2021 is provided in the following image:
        
        <p align="center">
          <img src="https://raw.githubusercontent.com/PIC4SeR/MPOSE2021_Dataset/master/docs/mpose2021_summary.png" alt="MPOSE2021 Summary" width="600">
        </p>
        
        Below, the steps to install the ```mpose``` library and obtain sequences are explained. Source code can be found in the [MPOSE2021 repository](https://github.com/PIC4SeRCentre/MPOSE2021_Dataset).
        
        ### Installation
        
        Install MPOSE2021 as python package from [PyPi](https://pypi.org/project/mpose)
        ```
        pip install mpose
        ```
        
        ### Getting Started
        
        ```python
        # import package
        import mpose
        
        # initialize and download data
        dataset = mpose.MPOSE(pose_extractor='openpose', 
                              split=1, 
                              transform='scale_and_center', 
                              data_dir='./data/')
        
        # print data info 
        dataset.get_info()
        
        # get data samples (as numpy arrays)
        X_train, y_train, X_test, y_test = dataset.get_dataset()
        ```
        
        [![asciicast](https://asciinema.org/a/4dXzjbZUoxXM6d3o0aNumGLr7.svg)](https://asciinema.org/a/4dXzjbZUoxXM6d3o0aNumGLr7)
        
        Check out our [Colab Notebook Tutorial](https://colab.research.google.com/drive/1_v3DYwgZPMCiELtgiwMRYxQzcYGdSWFH?usp=sharing) for quick hands-on examples.
        
        ### References
        
        MPOSE2021 is part of a [paper published by the Pattern Recognition Journal](https://authors.elsevier.com/a/1eH6s77nKcvmg) (Elsevier), and is intended for scientific research purposes.
        If you want to use MPOSE2021 for your research work, please also cite [1-10].
        
        ```
        @article{mazzia2021action,
          title={Action Transformer: A Self-Attention Model for Short-Time Pose-Based Human Action Recognition},
          author={Mazzia, Vittorio and Angarano, Simone and Salvetti, Francesco and Angelini, Federico and Chiaberge, Marcello},
          journal={Pattern Recognition},
          pages={108487},
          year={2021},
          publisher={Elsevier}
        }
        ```
        
        [1] Angelini, F., Fu, Z., Long, Y., Shao, L., & Naqvi, S. M. (2019). 2D Pose-Based Real-Time Human Action Recognition With Occlusion-Handling. IEEE Transactions on Multimedia, 22(6), 1433-1446.
        
        [2] Angelini, F., Yan, J., & Naqvi, S. M. (2019, May). Privacy-preserving Online Human Behaviour Anomaly Detection Based on Body Movements and Objects Positions. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8444-8448). IEEE.
        
        [3] Angelini, F., & Naqvi, S. M. (2019, July). Joint RGB-Pose Based Human Action Recognition for Anomaly Detection Applications. In 2019 22th International Conference on Information Fusion (FUSION) (pp. 1-7). IEEE.
        
        [4] Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2019). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE transactions on pattern analysis and machine intelligence, 43(1), 172-186.
        
        [5] Gorelick, L., Blank, M., Shechtman, E., Irani, M., & Basri, R. (2007). Actions as Space-Time Shapes. IEEE transactions on pattern analysis and machine intelligence, 29(12), 2247-2253.
        
        [6] Starck, J., & Hilton, A. (2007). Surface Capture for Performance-Based Animation. IEEE computer graphics and applications, 27(3), 21-31.
        
        [7] Weinland, D., Özuysal, M., & Fua, P. (2010, September). Making Action Recognition Robust to Occlusions and Viewpoint Changes. In European Conference on Computer Vision (pp. 635-648). Springer, Berlin, Heidelberg.
        
        [8] Schuldt, C., Laptev, I., & Caputo, B. (2004, August). Recognizing Human Actions: a Local SVM Approach. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. (Vol. 3, pp. 32-36). IEEE.
        
        [9] Xia, L., Chen, C. C., & Aggarwal, J. K. (2012, June). View Invariant Human Action Recognition using Histograms of 3D Joints. In 2012 IEEE computer society conference on computer vision and pattern recognition workshops (pp. 20-27). IEEE.
        
        [10] Chen, C., Jafari, R., & Kehtarnavaz, N. (2015, September). UTD-MHAD: A Multimodal Dataset for Human Action Recognition utilizing a Depth Camera and a Wearable Inertial Sensor. In 2015 IEEE International conference on image processing (ICIP) (pp. 168-172). IEEE.
        
        [11] Papandreou, G., Zhu, T., Chen, L. C., Gidaris, S., Tompson, J., & Murphy, K. (2018). Personlab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 269-286).
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