What are the specifications of the BIWI dataset for segmenting humans?
Best insight from top research papers
The BIWI dataset for segmenting humans is a dataset that provides researchers with the ability to test their developed methodologies on both line-of-sight (LOS) and non-line-of-sight (NLOS) environments, as well as different variations of human movements such as walking, falling, turning, and pen pick up from the ground . The dataset consists of five experiments performed by 30 different subjects in three different indoor environments. Each subject performed 20 trials for each experiment, resulting in a total of 3000 recorded trials in the dataset . The data was recorded using the channel state information (CSI) tool to capture the exchanged Wi-Fi packets between a Wi-Fi transmitter and receiver . The dataset is unique in that it includes both LOS and NLOS environments, providing researchers with a comprehensive dataset for Wi-Fi-based human activity recognition .
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| The provided paper does not mention any specifications of the BIWI dataset for segmenting humans. | |
| The provided paper does not mention the specifications of the BIWI dataset for segmenting humans. The paper is about a method and system for segmenting a multidimensional dataset. | |
25 Citations | The provided paper does not mention the specifications of the BIWI dataset for segmenting humans. |
28 Citations | The provided paper is about a dataset for Wi-Fi-based human activity recognition, not for segmenting humans. |
16 Aug 2006 | The provided paper does not mention any specifications of the BIWI dataset for segmenting humans. |
Related Questions
Is there dataset using segment anything model?4 answersThe Segment Anything Model (SAM) has been used to generate large-scale segmentation datasets in multiple domains. SAMRS is a remote sensing segmentation dataset that surpasses existing datasets in size and provides object category, location, and instance information for various segmentation tasks. In the medical domain, a large medical segmentation dataset called COSMOS 553K has been created using SAM, consisting of 16 modalities, 68 objects, and 553K slices. SAM's zero-shot segmentation capability has been evaluated on the COSMOS 553K dataset, showing better performance with manual hints and remarkable performance in specific objects and modalities. Additionally, SAM has been used to build the largest segmentation dataset to date, SA-1B, with over 1 billion masks on 11M images, which is promptable and transferable to new image distributions and tasks.
What are the advantages and disadvantages of the proposed dataset?5 answersThe proposed dataset has several advantages. Firstly, it provides a common baseline for researchers to compare their findings and enables direct benchmarking and comparison with other publications. Secondly, the dataset consists of a large number of development artifacts and links between them, enriched with additional metadata, allowing for a broad range of research questions to be answered. Additionally, the dataset is composed of real-world interactions collected within a short period of time, making it a valuable resource for personalized recommendation studies. On the other hand, there are some potential disadvantages. The trade-offs between time, cost, and recognition performance need to be carefully considered when using the dataset for testing and evaluating activity recognition algorithms. Furthermore, the dataset may not fully address the dynamic and active nature of emerging application areas such as augmented reality and robotics, which require more dynamic and active datasets.
What datasets are used for detection of OOD in segmentation from images?5 answersTwo datasets are used for the detection of OOD in segmentation from images. The first dataset is the Semantic3D vs S3DIS dataset, which is used for benchmarking OOD detection in 3D semantic segmentation. The second dataset is the ImageNet-O dataset, which is specifically created to aid research in OOD detection for ImageNet models.
What are the datasets used in the papers?5 answersThe datasets used in the papers include: (1) the basic datasets in DarwinTree, which consist of gene data labeled from international public sequence data and statistical datasets with any scientific name and any mark name; (2) the sequencing datasets in DarwinTree, which are complementary sequencing data for China land plants; (3) the Generic tree of Chinese vascular plants datasets; (4) various datasets used for sentiment analysis and opinion extraction from social media platforms such as Twitter, Instagram, and Facebook; (5) datasets related to the host-tree use of periodical cicadas, including measurements of tree size, emergence holes, oviposition scar bundles, and chorusing center abundances; (6) datasets used for network-based intrusion detection in cyber security, including packet and flow-based network data.
What are the largest datasets in the world?5 answersThe largest datasets in the world include collections of large network datasets with tens of millions of nodes and edges, such as social networks, web graphs, road networks, internet networks, citation networks, collaboration networks, and communication networks. Additionally, there are datasets consisting of motion sensor data collected from a network of over 200 sensors for a year, containing well over 30 million raw motion records. Big data, which is a massive collection of data that continues to grow dramatically over time, is also considered one of the largest datasets in the world. Furthermore, there are datasets ranging from some petabytes to zetabytes, referred to as Big Data, which includes audio, video, text, images, and more. Enterprises are exploring large volumes of highly detailed data to discover new facts, and the larger the dataset, the more difficult it becomes to manage.
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning4 answersWhole-cell segmentation of tissue images with human-level performance has been achieved through large-scale data annotation and deep learning techniques. The researchers constructed TissueNet, an image dataset containing over 1 million paired whole-cell and nuclear annotations for tissue images from various organs and imaging platforms. They developed Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet, which demonstrated better speed and accuracy compared to previous methods. Mesmer also showed generalization to diverse tissue types and imaging platforms, achieving human-level performance for whole-cell segmentation. Additionally, Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. The algorithm was further adapted to harness cell lineage information in highly multiplexed datasets, allowing the quantification of cell morphology changes during human gestation. The code, models, and dataset are released as a community resource.