Artificial Intelligence
Internet-of-Things (IoT) technology such as Surveillance cameras are becoming a widespread feature of citizens' life. At the same time, the fear of crime in public spaces (e.g., terrorism) is ever-present and increasing but currently only a small number of studies researched automatic recognition of criminal incidents featuring artificial intelligence (AI), e.g., based on deep learning and computer vision. This is due to the fact that little to none real data is available due to legal and privacy regulations. Consequently, it is not possible to train and test deep learning models.
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This dataset is associated with TODOS: Thermal sensOr Data-driven Occupancy Estimation System for Smart Buildings. It is a novel system for estimating occupancy in intelligent buildings, TODOS uses a low-cost, low-power thermal sensor array along with a passive infrared sensor. We introduce a novel data processing pipeline that allows us to automatically extract features from the thermal images using an artificial neural network. Through an extensive experimental evaluation, we show that TODOS provides occupancy detection accuracy of 98% to 100% under different scenarios.
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Dataset for validation of a new magnetic field-based wearable breathing sensor (MAG), which uses the movement of the chest wall as a surrogate measure of respiratory activity. Based on the principle of variation in magnetic field strength with the distance from the source, this system explores Hall effect sensing, paired with a permanent magnet, embedded in a chest strap.
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The daily case data for HFMD (Hand-Foot-and-Mouth Disease) and herpangina were collected from the Children’s Hospital of Chongqing Medical University, a national clinical medical research center of China, spanning from January 1, 2015, to December 31, 2021. This dataset contains the timestamp of each patient's discharge, resulting in a total of 109,096 records for HFMD and 133,869 records for herpangina.
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This dataset, presents the results of motion detection experiments conducted on five distinct datasets sourced from changedetection.net: bungalows, boats, highway, fall and pedestrians. The motion detection process was executed using two distinct algorithms: the original ViBe algorithm proposed by Barnich et al. (G-ViBe) and the CCTV-optimized ViBe algorithm known as α-ViBe.
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This dataset, presents the results of motion detection experiments conducted on five distinct datasets sourced from changedetection.net: bungalows, boats, highway, fall and pedestrians. The motion detection process was executed using two distinct algorithms: the original ViBe algorithm proposed by Barnich et al. (G-ViBe) and the CCTV-optimized ViBe algorithm known as α-ViBe.
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Most existing video text spotting benchmarks focus on evaluating a single language and scenario with limited data.
In this work, we introduce a large-scale, Bilingual, Open World Video text benchmark dataset (BOVText V2). There are four
features for BOVText V2. Firstly, we provide 2,000+ videos with more than 1,750,000+ frames, 25 times larger than the existing
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In this study, we extend the existing thermal infrared face dataset (TIF) by improving the diversity and quality of data. More specifically, the new data contains more acquisition periods with significant differences among ambient temperature periods and a slow change in ambient temperature within each period. At the same time, we provide the corresponding visible images of the infrared images to assist in face detection and face depth estimation. For noise reduction, we calculated the average facial temperature of the short-term population and determined upper and lower limits.
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Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV.
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Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.
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