Each HPDmobile data acquisition system consists of: The sensor hubs run a Linux based operating system and serve to collect and temporarily store individual sensor readings. Download: Data Folder, Data Set Description. Other studies show that by including occupancy information in model predictive control strategies, residential energy use could be reduced by 1339%6,7. WebComputing Occupancy grids with LiDAR data, is a popular strategy for environment representation. Days refers to the number of days of data that were released from the home, while % Occ refers to the percentage of time the home was occupied by at least one person (for the days released). The sensor is calibrated prior to shipment, and the readings are reported by the sensor with respect to the calibration coefficient that is stored in on-board memory. Work fast with our official CLI. Structure gives the tree structure of sub-directories, with the final entry in each section describing the data record type. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. See Fig. WebThis is the dataset Occupancy Detection Data Set, UCI as used in the article how-to-predict-room-occupancy-based-on-environmental-factors Content WebThe publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable Images include the counts for dark images, while % Dark gives the percentage of collected images that were counted as dark with respect to the total possible per day. The results are given in Fig. The DYD data is collected from ecobee thermostats, and includes environmental and system measurements such as: runtime of heating and cooling sources, indoor and outdoor relative humidity and temperature readings, detected motion, and thermostat schedules and setpoints. To address this, we propose a tri-perspective view (TPV) representation which National Library of Medicine A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. GitHub is where people build software. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Webance fraud detection method utilizing a spatiotemporal constraint graph neural network (StGNN). Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. Radar provides depth perception through soft materials such as blankets and other similar coverings that cover children. Are you sure you want to create this branch? Using a constructed data set to directly train the model for detection, we can obtain information on the quantity, location and area occupancy of rice panicle, all without concern for false detections. We created a synthetic dataset to investigate and benchmark machine learning approaches for the application in the passenger compartment regarding the challenges introduced in Section 1 and to overcome some of the shortcomings of common datasets as explained in Section 2. Next, processing to validate the data and check for completeness was performed. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14920131. Are you sure you want to create this branch? A review of building occupancy measurement systems. For instance, false positives (the algorithm predicting a person was in the frame when there was no one) seemed to occur more often on cameras that had views of big windows, where the lighting conditions changed dramatically. Huchuk B, Sanner S, OBrien W. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data. Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. In The 2nd Workshop on Figure4 shows examples of four raw images (in the original 336336 pixel size) and the resulting downsized images (in the 3232 pixel size). 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. See Fig. The dataset has camera-based occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count. (d) Average pixel brightness: 10. Thrsh gives the hub specific cut-off threshold that was used to classify the image as occupied or vacant, based on the output from the YOLOv5 algorithm. 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. The model integrates traffic density, traffic velocity and duration of instantaneous congestion. Audio files are named based on the beginning second of the file, and so the file with name 2019-10-18_002910_BS5_H5.csv was captured from 12:29:10 AM to 12:29:19 AM on October 18, 2019 in H6 on hub 5 (BS5). In the process of consolidating the environmental readings, placeholder timestamps were generated for missing readings, and so each day-wise CSV contains exactly 8,640 rows of data (plus a header row), although some of the entries are empty. Luis M. Candanedo, Vronique Feldheim. Summary of the completeness of data collected in each home. This outperforms most of the traditional machine learning models. 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. See Fig. While many datasets exist for the use of object (person) detection, person recognition, and people counting in commercial spaces1921, the authors are aware of no publicly available datasets which capture these modalities for residential spaces. Performance of a k-nearest neighbors classifier on unprocessed audio (P0), and audio data as publicly available in the database (P1). At the end of the collection period, occupancy logs from the two methods (paper and digital) were reviewed, and any discrepancies or questionable entries were verified or reconciled with the occupants. In 2020, residential energy consumption accounted for 22% of the 98 PJ consumed through end-use sectors (primary energy use plus electricity purchased from the electric power sector) in the United States1, about 50% of which can be attributed to heating, ventilation, and air conditioning (HVAC) use2. Research, design, and testing of the system took place over a period of six months, and data collection with both systems took place over one year. Web[4], a dataset for parking lot occupancy detection. Luis M. Candanedo, Vronique Feldheim. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. Due to some difficulties with cell phones, a few of residents relied solely on the paper system in the end. To achieve the desired higher accuracy, proposed OccupancySense model detects human presence and predicts indoor occupancy count by the fusion of Internet of Things (IoT) based indoor air quality (IAQ) data along with static and dynamic context data which is a unique approach in this domain. 7a,b, which were labeled as vacant at the thresholds used. Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing. & Hirtz, G. Improved person detection on omnidirectional images with non-maxima suppression. Room occupancy detection is crucial for energy management systems. After collection, data were processed in a number of ways. WebAccurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. to use Codespaces. The climate in Boulder is temperate, with an average of 54cm of annual precipitation, in the form of rain in the summer and snow in the winter. (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). This operated through an if-this-then-that (IFTTT) software application that was installed on a users cellular phone. Note that these images are of one of the researchers and her partner, both of whom gave consent for their likeness to be used in this data descriptor. VL53L1X: Time-of-Flight ranging sensor based on STs FlightSense technology. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. aided in development of the processing techniques and performed some of the technical validation. Missing data are represented as blank, unfilled cells in the CSVs. The project was part of the Saving Energy Nationwide in Structures with Occupancy Recognition (SENSOR) program, which was launched in 2017 to develop user-transparent sensor systems that accurately quantify human presence to dramatically reduce energy use in commercial and residential buildings23. If nothing happens, download GitHub Desktop and try again. Cite this APA Author BIBTEX Harvard Standard RIS Vancouver (a) H1: Main level of three-level home. Each sensor hub is connected to an on-site server through a wireless router, all of which are located inside the home being monitored. About Dataset Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. (b) H2: Full apartment layout. Images from both groups (occupied and vacant) were then randomly sampled, and the presence or absence of a person in the image was verified manually by the researchers. Timestamp format is consistent across all data-types and is given in YY-MM-DD HH:MM:SS format with 24-hour time. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Because of IRB restrictions, no homes with children under the age of 18 were included. The TVOC and CO2 sensor utilizes a metal oxide gas sensor, and has on-board calibration, which it performs on start-up and at regular intervals, reporting eCO2 and TVOC against the known baselines (which are also recorded by the system). For a number of reasons, the audio sensor has the lowest capture rate. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. Each home was to be tested for a consecutive four-week period. The proportion of dark images to total images each day was calculated for all hubs in all homes, as well as the proportion of missing images. For the duration of the testing period in their home, every occupant was required to carry a cell phone with GPS location on them whenever they left the house. The median cut-off value was 0.3, though the values ranged from 0.2 to 0.6. HHS Vulnerability Disclosure, Help First, a geo-fence was deployed for all test homes. To generate the different image sizes, the 112112 images were either downsized using bilinear interpolation, or up-sized by padding with a white border, to generate the desired image size. government site. 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. Based on the reviewed research frameworks, occupancy detection in buildings can be performed using data collected from either the network of sensors (i.e., humidity, temperature, CO 2, etc. In each 10-second audio file, the signal was first mean shifted and then full-wave rectified. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. In terms of device, binocular cameras of RGB and infrared channels were applied. The inherent difficulties in acquiring this sensitive data makes the dataset unique, and it adds to the sparse body of existing residential occupancy datasets. Waymo is in a unique position to contribute to the research community with some of the largest and most diverse autonomous driving datasets ever released. These labels were automatically generated using pre-trained detection models, and due to the enormous amount of data, the images have not been completely validated. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. R, Rstudio, Caret, ggplot2. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. The authors declare no competing interests. 1University of Colorado Boulder, Department of Civil, Environmental and Architectural Engineering, Boulder, 80309-0428 United States, 2Iowa State University, Department of Mechanical Engineering, Ames, 50011 United States, 3National Renewable Energy Laboratory, Golden, 80401 United States, 4Renewable and Sustainable Energy Institute, Boulder, 80309 United States. Also note that when training and testing the models you have to use the seed command to ensure reproducibility. 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