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11 results for “Action Recognition”
HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction
<p>A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.</p>
The DARRL dataset: Demonstrations for Action Recognition and Robot Learning
<p>The DARRL dataset (Demonstrations for Action Recognition and Robot Learning) is a collection of 760 RGB-D videos of humans performing various manipulation tasks. It is provided with object and action annotations (in the COCO format) for 30 of those videos; segmentation masks are also provided.</p> <p>It can also be used as a basis for learning from demonstrations for a robotic arm, for instance.</p> <p> </p> <p>This work is supported by Région Pays de la Loire.</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Community Engagement for Early Recognition and Immediate Action in Stroke
ClinicalTrials.gov study NCT02301299. IPD Sharing: Not stated. Countries: 1. Publications: 1.
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics
<p><strong>Objectives</strong></p> <p>We introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.<br> Read <strong>00-README.txt</strong> for detailed download instructions.</p> <p>More details on the dataset at <a href="https://github.com/vhavard/InHARD">https://github.com/vhavard/InHARD</a></p> <p>This work has been performed at the CESI LINEACT : <a href="https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/">https://recherche.cesi.fr/inhard-industrial-human-action-recognition-dataset/</a></p>
MPOSE2021: a Dataset for Short-Time Pose-Based Human Action Recognition
<p>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.</p> <p>MPOSE2021 is developed as an evolution of the MPOSE Dataset [1-3]. It is made by human pose data detected by <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> [4] and <a href="https://github.com/tensorflow/tfjs-models/tree/master/posenet">Posenet</a> [11] 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. Generated sequences have a number of frames between 20 and 30. Sequences are obtained by cutting the so-called Precursor videos (video from the above-mentioned datasets), with non-overlapping sliding windows. Frames where OpenPose/PoseNet 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.</p> <p>More information about the dataset can be found in the <a href="https://github.com/PIC4SeRCentre/MPOSE2021_Dataset">MPOSE2021 repository</a>, also providing a user-friendly Python package to import and use the dataset by just running the command</p> <pre><code>pip install mpose</code></pre> <p> </p> <p><strong>Data Structure</strong></p> <p>The repository contains 3 datasets for each pose extractor (namely 1, 2 and 3) which consist of the same data divided in different train/test splits. Each dataset contains X and y numpy arrays for both training and testing. X has the following shape:</p> <pre>(B, T, K, C)</pre> <p>where</p> <ul> <li>B is the batch number;</li> <li>T (= 30) is the duration of the sequences in frames (zero-padded in the case of shorter sequences);</li> <li>K (= 17 for PoseNet and 25 for OpenPose) is the number of pose keypoints;</li> <li>C (= 3) is the number of channels, comprehending 2D keypoint coordinates (x,y) in the original video reference frame and the keypoint confidence (p <= 1)</li> </ul> <p>The .txt files specifying the metadata associated with the split samples are also included.</p> <p><strong>References</strong></p> <p>MPOSE2021 is part of a <a href="https://authors.elsevier.com/a/1eH6s77nKcvmg">paper published by the Pattern Recognition Journal</a> (Elsevier), and is intended for scientific research purposes. If you want to use MPOSE2021 for your research work, please also cite [1-11].</p> <pre><code>@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} } </code></pre> <p>[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.</p> <p>[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.</p> <p>[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.</p> <p>[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.</p> <p>[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.</p> <p>[6] Starck, J., & Hilton, A. (2007). Surface Capture for Performance-Based Animation. IEEE computer graphics and applications, 27(3), 21-31.</p> <p>[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.</p> <p>[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.</p> <p>[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.</p> <p>[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.</p> <p>[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).</p>
A Trimodal Dataset: RGB, Thermal, and Depth for Human Segmentation and Action Recognition
<p>Computer vision research and popular datasets are predominantly based on the RGB modality. However, traditional RGB datasets have limitations in lighting conditions and raise privacy concerns. Integrating or substituting with thermal and depth data offers a more robust and privacy-preserving alternative. We present a public trimodal dataset comprising registered sequences of RGB, depth, and thermal data. The dataset encompasses 10 unique environments, 18 camera angles, 101 shots, and 15,618 frames which include human masks for semantic segmentation and dense labels for action classification and scene understanding. We discuss the system setup, including sensor configuration and calibration, as well as the process of generating ground truth annotations. On top, we conduct a quality analysis of our proposed dataset and provide benchmark models as reference points for human segmentation and action recognition. By employing only modalities of thermal and depth, these models yield improvements in both human segmentation and action classification.</p>
The pain hidden in your hands: facial expression of pain reduces the influence of goal-related information in action recognition
<p>data and code associated to manuscript entitled "The pain hidden in your hands: facial expression of pain reduces the influence of goal-related information in action recognition"</p>
InHARD-DT - Industrial Human Action Recognition Dataset - Digital Twin
<p>This paper explores the use of a Digital Twin of a real industrial workstation involving assembly tasks with a robotic arm interfaced with Virtual Reality (VR) to extract a digital human model. The DT simulates assembly operations performed by humans aiming to generate self-labeled data. Thereby, a Human Action Recognition dataset named InHARD-DT was created to validate a real use case in which we use the acquired auto-labeled DT data of the virtual representation of the InHARD dataset to train a Spatial–Temporal Graph Convolutional Neural Network with skeletal data on one hand. On the other hand, the Physical Twin (PT) data of the InHARD dataset was used for testing. Therefore, we introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset - Digital Twin” (InHARD-DT) from a real-world setting for industrial human action recognition. </p> <p>We invited 12 distinct subjects from the LINEACT laboratory (4 females and 8 males) for the DT data collection to perform the same assembly tasks of the InHARD dataset (link below) in Virtual Reality via a VR application of an industrial real workstation. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations. It can be used also in cross-validation scenarios where the training phase can be done using the Physical Twin (PT) data of the InHARD dataset (real world scenarios) and then test using Digital Twin (DT) data of the InHARD-DT dataset which is the main objective of this paper. </p>
Action recognition and object detection dataset for firearm-related actions
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