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3 results for “Pose Classification”
CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification
<p>This refers to the gan-generated dataset for the paper" CSI4Free: GAN-Augmented mmWave CSI for<br>Improved Pose Classification.</p> <p>Abstract:In recent years, Joint Communication and Sensing (JC&S), has demonstrated significant success, particularly in utilizing sub-6 GHz frequencies with commercial-off-the-shelf (COTS) Wi-Fi devices for applications such as localization, gesture recognition, and pose classification. Deep learning and the existence of large public datasets has been pivotal in achieving such results. However, at mmWave frequencies (30-300 GHz), which has shown potential for more accurate sensing performance, there is a noticeable lack of research in the domain of COTS Wi-Fi sensing. Challenges such as limited research hardware, the absence of large datasets, limited functionality in COTS hardware, and the complexities of data collection present obstacles to a comprehensive exploration of this field. In this work, we aim to address these challenges by developing a method that can generate synthetic mmWave channel state information (CSI) samples. In particular, we use a generative adversarial network (GAN) on an existing dataset, to generate 30,000 additional CSI samples. The augmented samples exhibit a remarkable degree of consistency with the original data, as indicated by the notably high GAN-train and GAN-test scores. Furthermore, we integrate the augmented samples in training a pose classification model. We observe that the augmented samples complement the real data and improve the generalization of the classification model.</p> <p>The repository is available here: https://github.com/nisarnabeel/Dataset-GAN-Augmented-mmWave-CSI-for-improved-pose-classification</p> <p> </p> <p>Paper Link:https://ieeexplore.ieee.org/document/10646223/</p>
SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification
<p>The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," <em>2020 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284.</p> <p>BibTeX citation:</p> <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>
Achieving Peak Performance: Unveiling the Best Hyperparameter Combination for Accurate Skin Disorders Pose Classification with Convolutional Neural Network
<p>This material has presented on 2nd International Conference on Advance Research in Social and Economic Science in October 25, 2023.</p>
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