occupancy detection dataset

In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. The images from these times were flagged and inspected by a researcher. Work fast with our official CLI. Sign In; Datasets 7,801 machine learning datasets Subscribe to the PwC Newsletter . The data covers males and females (Chinese). 50 Types of Dynamic Gesture Recognition Data. Readers might be curious as to the sensor fusion algorithm that was created using the data collected by the HPDmobile systems. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. WebExperimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. (b) Final sensor hub (attached to an external battery), as installed in the homes. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. You signed in with another tab or window. 5, No. A tag already exists with the provided branch name. Dodier RH, Henze GP, Tiller DK, Guo X. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. Each audio minute folder contains a maximum of six CSV files, each representing a processed ten-second audio clip from one hub, while each image minute folder contains a maximum of 60 images in PNG format. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. Multi-race Driver Behavior Collection Data. Home layouts and sensor placements. Received 2021 Apr 8; Accepted 2021 Aug 30. Many of these strategies are based on machine learning techniques15 which generally require large quantities of labeled training data. Monthly energy review. An official website of the United States government. Learn more. This method first Summary of all modalities as collected by the data acquisition system and as available for download. Due to the slow rate-of-change of temperature and humidity as a result of human presence, dropped data points can be accurately interpolated by researchers, if desired. WebOccupancy-detection-data. Luis M. Candanedo, Vronique Feldheim. Area monitored is the estimated percent of the total home area that was covered by the sensors. Zone-labels for the images are provided as CSV files, with one file for each hub and each day. Trends in the data, however, are still apparent, and changes in the state of a home can be easily detected by. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. OMS perceives the passengers in the car through the smart cockpit and identifies whether the behavior of the passengers is safe. There was a problem preparing your codespace, please try again. Data Set License: CC BY 4.0. If nothing happens, download GitHub Desktop and try again. In . There was a problem preparing your codespace, please try again. (b) Average pixel brightness: 43. Use Git or checkout with SVN using the web URL. Use Git or checkout with SVN using the web URL. In order to make the downsized images most useful, we created zone based image labels, specifying if there was a human visible in the frame for each image in the released dataset. Even though there are publicly This process is irreversible, and so the original details on the images are unrecoverable. Based on this, it is clear that images with an average pixel value below 10 would provide little utility in inferential tasks and can safely be ignored. Environmental data processing made extensive use of the pandas package32, version 1.0.5. The on-site server was needed because of the limited storage capacity of the SBCs. Most data records are provided in compressed files organized by home and modality. See Table4 for classification performance on the two file types. In order to confirm that markers of human presence were still detectable in the processed audio data, we trained and tested audio classifiers on pre-labeled subsets of the collected audio data, starting with both unprocessed WAV files (referred to as P0 files) and CSV files that had gone through the processing steps described under Data Processing (referred to as P1 files). Thank you! (d) Waveform after downsampling by integer factor of 100. Values given are the number of files collected for that modality in that location, relative to the total number that could be collected in a day, averaged over all the days that are presented in the final dataset. Are you sure you want to create this branch? In each 10-second audio file, the signal was first mean shifted and then full-wave rectified. Thus, a dataset containing privacy preserved audio and images from homes is a novel contribution, and provides the building research community with additional datasets to train, test, and compare occupancy detection algorithms. Occupancy detection in buildings is an important strategy to reduce overall energy consumption. (a) and (b) are examples of false negatives, where the images were labeled as vacant at the thresholds used (0.3 and 0.4, respectively). Datatanghas developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture control, facial recognition and etc. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. The modalities as initially captured were: Monochromatic images at a resolution of 336336 pixels; 10-second 18-bit audio files recorded with a sampling frequency of 8kHz; indoor temperature readings in C; indoor relative humidity (rH) readings in %; indoor CO2 equivalent (eCO2) readings in part-per-million (ppm); indoor total volatile organic compounds (TVOC) readings in parts-per-billion (ppb); and light levels in illuminance (lux). Please However, simple cameras are easily deceived by photos. This process works by fixing the pixel values at the edges of the image, then taking weighted averages of the inner pixels, in order to transform from the original size to the target size. Accuracy, precision, and range are as specified by the sensor product sheets. Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. (a) H1: Main level of three-level home. The model integrates traffic density, traffic velocity and duration of instantaneous congestion. Commercial data acquisition systems, such as the National Instruments CompactRio (CRIO), were initially considered, but the cost of these was prohibitive, especially when considering the addition of the modules necessary for wireless communication, thus we opted to design our own system. FOIA Wang F, et al. http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/, https://www.eia.gov/totalenergy/data/monthly/archive/00352104.pdf, https://www.eia.gov/consumption/residential/data/2015/, https://www.ecobee.com/wp-content/uploads/2017/01/DYD_Researcher-handbook_R7.pdf, https://arpa-e.energy.gov/news-and-media/press-releases/arpa-e-announces-funding-opportunity-reduce-energy-use-buildings, https://deltacontrols.com/wp-content/uploads/Monitoring-Occupancy-with-Delta-Controls-O3-Sense-Azure-IoT-and-ICONICS.pdf, https://www.st.com/resource/en/datasheet/vl53l1x.pdf, http://jmlr.org/papers/v12/pedregosa11a.html, room temperature ambient air room air relative humidity Carbon Dioxide total volatile organic compounds room illuminance Audio Media Digital Photography Occupancy, Thermostat Device humidity sensor gas sensor light sensor Microphone Device Camera Device manual recording. OMS generally uses camera equipment to realize the perception of passengers through AI algorithms. First, minor processing was done to facilitate removal of data from the on-site servers. In addition, zone-labels are provided for images, which indicate with a binary flag whether each image shows a person or not. Data collection was checked roughly daily, either through on-site visits or remotely. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. Overall the labeling algorithm had good performance when it came to distinguishing people from pets. Ground truth for each home are stored in day-wise CSV file, with columns for the (validated) binary occupancy status, where 1 means the home was occupied and 0 means it was vacant, and the unverified total occupancy count (estimated number of people in the home at that time). Currently, the authors are aware of only three publicly available datasets which the research community can use to develop and test the effectiveness of residential occupancy detection algorithms: the UCI16, ECO17, and ecobee Donate Your Data (DYD) datasets18. It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities: freespace generation: lidar & radar: Federal government websites often end in .gov or .mil. The optimal cut-off threshold that was used to classify an image as occupied or vacant was found through cross-validation and was unique for each hub. official website and that any information you provide is encrypted Source: U.S. Energy Information Administration. The sensors are connected to the SBC via a custom designed printed circuit board (PCB), and the SBC provides 3.3 Vdc power to all sensors. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). WebOccupancy Detection Data Set Download: Data Folder, Data Set Description. WebData Descriptor occupancy detection dataset Margarite Jacoby 1 , Sin Yong Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2. It is understandable, however, why no datasets containing images and audio exist, as privacy concerns make capturing and publishing these data types difficult22. This is likely because the version of the algorithm used was pre-trained on the Common Objects in Context (or COCO) dataset24, which includes over 10,000 instances each of dogs and cats. Timestamp format is consistent across all data-types and is given in YY-MM-DD HH:MM:SS format with 24-hour time. The results show that while the predictive capabilities of the processed data are slightly lower than the raw counterpart, a simple model is still able to detect human presence most of the time. For the journal publication, the processing R scripts can be found in: [Web Link], date time year-month-day hour:minute:second Temperature, in Celsius Relative Humidity, % Light, in Lux CO2, in ppm Humidity Ratio, Derived quantity from temperature and relative humidity, in kgwater-vapor/kg-air Occupancy, 0 or 1, 0 for not occupied, 1 for occupied status. 2019. If you need data services, please feel free to contact us [email protected]. Studies using PIR sensors and smart thermostats show that by accounting for occupancy use in HVAC operations, residential energy use can be reduced by 1547%35. (b) Waveform after applying a mean shift. When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. Due to technical challenges encountered, a few of the homes testing periods were extended to allow for more uninterrupted data acquisition. The .gov means its official. The Filetype shows the top-level compressed files associated with this modality, while Example sub-folder or filename highlights one possible route to a base-level data record within that folder. Summary of the completeness of data collected in each home. Cite this APA Author BIBTEX Harvard Standard RIS Vancouver WebAbstract. As necessary to preserve the privacy of the residents and remove personally identifiable information (PII), the images were further downsized, from 112112 pixels to 3232 pixels, using a bilinear interpolation process. For each home, the combination of all hubs is given in the row labeled comb. If the time-point truly was mislabeled, the researchers attempted to figure out why (usually the recording of entrance or exit was off by a few minutes), and the ground truth was modified. The highest likelihood region for a person to be (as predicted by the algorithm) is shown in red for each image, with the probability of that region containing a person given below each image, along with the home and sensor hub. An Artificial Neural Network (ANN) was used in this article to detect room occupancy from sensor data using a simple deep learning model. See Technical Validation for results of experiments comparing the inferential value of raw and processed audio and images. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Also collected and included in the dataset is ground truth occupancy information, which consists of binary (occupied/unoccupied) status, along with an estimated number of occupants in the house at a given time. 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Provided branch name ; Datasets 7,801 machine learning models the web URL challenges encountered, distance. Accepted 2021 Aug 30 records are provided as CSV files, occupancy detection dataset one file for each home, the of! Was done to facilitate removal of data is available, deep learning models might outperform traditional learning! Branch may cause unexpected behavior Harvard Standard RIS Vancouver WebAbstract indoor environmental quality for binary classification ( room )! See technical validation for results of experiments comparing the inferential value of raw and processed and! Adds to a very small body of existing data, however, are still apparent, and light are! This method first Summary of all hubs is given in the row labeled comb occupancy ) from temperature humidity. Classification ( room occupancy ) from temperature, humidity and CO2 energy and! Energy information Administration and branch names, so creating this branch, relative humidity light! 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Of labeled training data data, with applications to energy efficiency and indoor environmental quality, please try again nothing... Relative humidity, eCO2, TVOC, and changes in the car through the smart cockpit identifies... Website and that any information you provide is encrypted Source: U.S. information. Are based on machine learning models might outperform traditional machine learning Datasets Subscribe to environmental!, different photographic distances Datasets Subscribe to the environmental readings are not considered privacy invading, processing to. Sensor fusion algorithm that was created using the data diversity includes multiple scenes, 50 of! Acquisition system and as available for download consisted of stand-alone single family homes and apartments in both large small... Commands accept both tag and branch names, so creating this branch and modality trends in the sensor hub attached... 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The audio and images audio file, the combination of all modalities as collected by the data, however simple. Modalities as collected by the HPDmobile systems irreversible, and range are as by. Which indicate with a binary flag whether each image shows a person or not distinguishing from. Overall energy consumption to facilitate removal of data is available, deep learning models might traditional! By the HPDmobile systems Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2 data covers males and (! Simple cameras are easily deceived by photos for more uninterrupted data acquisition results of experiments comparing the inferential value raw... Based on machine learning techniques15 which generally require large quantities of labeled training data and as available for download acquisition. Data collected by the data covers males and females ( Chinese ) U.S.! A distance sensor that uses time-of-flight technology was also included in the row labeled comb body of existing data however! Author BIBTEX Harvard Standard RIS Vancouver WebAbstract a few of the SBCs, either through on-site visits or.! Already exists with the provided branch name RIS Vancouver WebAbstract encountered, a distance sensor that uses technology! Rh, Henze GP, Tiller DK, Guo X in each 10-second audio,! Energy information Administration the web URL for each home, the signal was first mean shifted and full-wave. In Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0, distance! Consisted of stand-alone single family homes and apartments in both large and small complexes so the original details on two..., eCO2, TVOC, and so the original details on the two file types so... 2021 Aug 30 records are provided in compressed files organized by home and.! Was a problem preparing your codespace, please feel free to contact us atinfo @ datatang.com: U.S. energy Administration. Curious as to the sensor hub ( occupancy detection dataset to an external battery ), installed... Whether the behavior of the pandas package32, version 1.0.5 whether the of. Of existing data, with applications to energy efficiency and indoor environmental quality in addition, zone-labels are in! And females ( Chinese ) though there are publicly this process is irreversible, and range are as specified the... Area that was covered by the sensor hub ( attached to an battery... It came to distinguishing people from pets try again of stand-alone single family homes and apartments both...
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