occupancy detection dataset

In the last two decades, several authors have proposed different methods to render the sensed information into the grids, seeking to obtain computational efficiency or accurate environment modeling. The data includes multiple ages and multiple time periods. The goal was to cover all points of ingress and egress, as well as all hang-out zones. This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. Hubs were placed either next to or facing front doors and in living rooms, dining rooms, family rooms, and kitchens. 2 for home layouts with sensor hub locations marked. WebPeopleFinder Object Detection Dataset (v2, GoVap) by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. Work fast with our official CLI. The exception to this is data collected in H6, which has markedly lower testing accuracy on the P1 data. Before Each hub file or directory contains sub-directories or sub-files for each day. FOIA This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces. The occupants cover a range of ages and relationships and consisted of couples, roommate households, and one family with adult children who were home during part of the testing duration. The Pext: Build a Smart Home AI, What kind of Datasets We Need. (e) H4: Main level of two-level apartment. like this: from detection import utils Then you can call collate_fn Since higher resolution did have significantly better performance, the ground truth labeling was performed on the larger sizes (112112), instead of the 3232 sizes that are released in the database. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. (d) Waveform after downsampling by integer factor of 100. It includes a clear description of the data files. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The two sets of images (those labeled occupied and those labeled vacant by the YOLO algorithm) were each randomly sampled in an attempt to get an equal number of each type. Images were captured at a rate of 1 frame per second, while all environmental readings were captured every ten seconds. 2021. Please (seven weeks, asynchronous video lectures and assessments, plus six 1.5 hour synchronous sessions Thursdays from 7-8:30pm ET) 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. WebModern methods for vision-centric autonomous driving perception widely adopt the birds-eye-view (BEV) representation to describe a 3D scene. 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. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. A tag already exists with the provided branch name. put forward a multi-dimensional traffic congestion detection method in terms of a multi-dimensional feature space, which includes four indices, that is, traffic quantity density, traffic velocity, road occupancy and traffic flow. 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). Readers might be curious as to the sensor fusion algorithm that was created using the data collected by the HPDmobile systems. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. We also quantified detections of barred owls ( Strix varia ), a congeneric competitor and important driver of spotted owl population declines. WebOccupancy detection of an office room from light, temperature, humidity and CO2 measurements using TPOT (A Python tool that automatically creates and optimizes machine Overall the labeling algorithm had good performance when it came to distinguishing people from pets. In addition to the environmental readings shown in Table1, baseline measurements of TVOC and eCO2, as collected by the sensors, are also included in the files. The final distribution of noisy versus quiet files were roughly equal in each set, and a testing set was chosen randomly from shuffled data using a 70/30 train/test split. See Fig. These designations did not change throughout data collection, thus RS3 in home H1 is the same physical piece of hardware as RS3 in home H5. WebOccupancy Detection Computer Science Dataset 0 Overview Discussion 2 Homepage http://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ Description Three data sets are submitted, for training and testing. sign in Reliability of the environmental data collection rate (system performance) was fairly good, with higher than 95% capture rate for most modalities. del Blanco CR, Carballeira P, Jaureguizar F, Garca N. Robust people indoor localization with omnidirectional cameras using a grid of spatial-aware classifiers. & Hirtz, G. Improved person detection on omnidirectional images with non-maxima suppression. This method first Abstract: Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. With the exception of H2, the timestamps of these dark images were recorded in text files and included in the final dataset, so that dark images can be disambiguated from those that are missing due to system malfunction. Since the hubs were collecting images 24-hours a day, dark images accounted for a significant portion of the total collected, and omitting these significantly reduces the size of the dataset. The inherent difficulties in acquiring this sensitive data makes the dataset unique, and it adds to the sparse body of existing residential occupancy datasets. The homes and apartments tested were all of standard construction, representative of the areas building stock, and were constructed between the 1960s and early 2000s. Most data records are provided in compressed files organized by home and modality. U.S. Energy Information Administration. binary classification (room occupancy) from Temperature,Humidity,Light and CO2. occupancy was obtained from time stamped pictures that were taken every minute. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Luis M. Candanedo, Vronique Feldheim. Subsequent review meetings confirmed that the HSR was executed as stated. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). Each home was to be tested for a consecutive four-week period. (a) Raw waveform sampled at 8kHz. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. You signed in with another tab or window. 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. Audio processing steps performed on two audio files. The dataset has camera-based occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count. All were inexpensive and available to the public at the time of system development. 3.1 Synthetic objects Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. Classification was done using a k-nearest neighbors (k-NN) algorithm. All images in the labeled subsets, however, fell above the pixel value of 10 threshold. There was a problem preparing your codespace, please try again. Zone-labels for the images are provided as CSV files, with one file for each hub and each day. Finally, the signal was downsampled by a factor of 100 and the resulting audio signal was stored as a CSV file. 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. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. The authors wish the thank the following people: Cory Mosiman, for his instrumental role in getting the data acquisition system set up; Hannah Blake and Christina Turley, for their help with the data collection procedures; Jasmine Garland, for helping to develop the labeled datasets used in technical validation; the occupants of the six monitored homes, for letting us invade their lives. Because data could have been taken with one of two different systems (HPDred or HPDblack), the sensor hubs are referred to by the color of the on-site server (red or black). The method that prevailed is a hierarchical approach, in which instantaneous occupancy inferences underlie the higher-level inference, according to an auto-regressive logistic regression process. 7d,e), however, for the most part, the algorithm was good at distinguishing people from pets. Environmental data are stored in CSV files, with one days readings from a single hub in each CSV. Each day-wise CSV file contains a list of all timestamps in the day that had an average brightness of less than 10, and was thus not included in the final dataset. If nothing happens, download Xcode and try again. (a) Average pixel brightness: 106. Energy and Buildings. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. Home layouts and sensor placements. Virtanen P, et al. (g) H6: Main level of studio apartment with lofted bedroom. Built for automotive perception system developers, Prism AI is a collaborative ecosystem providing seven object detection classes, visible-and-thermal image fusion, advanced thermal image processing capabilities, new shadow mode recording capabilities, batch data ingestion, and more. However, simple cameras are easily deceived by photos. In one hub (BS2) in H6, audio was not captured at all, and in another (RS2 in H5) audio and environmental were not captured for a significant portion of the collection period. Browse State-of-the-Art Datasets ; Methods; More . Rice yield is closely related to the number and proportional area of rice panicles. 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. Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. The results are given in Fig. This repository hosts the experimental measurements for the occupancy detection tasks. Due to the increased data available from detection sensors, machine learning models can be created and used Example of the data records available for one home. The images from these times were flagged and inspected by a researcher. The code base that was developed for data collection with the HPDmobile system utilizes a standard client-server model, whereby the sensor hub is the server and the VM is the client. All Rights Reserved. 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. Monthly energy review. Three data sets are submitted, for training and testing. See Table2 for a summary of homes selected. The growing penetration of sensors has enabled the devel-opment of data-driven machine learning models for occupancy detection. Five images that were misclassified by the YOLOv5 labeling algorithm. ), mobility sensors (i.e., passive infrared (PIR) sensors collecting mobility data) smart meters (i.e., energy consumption footprints) or cameras (i.e., visual The ten-second sampling frequency of the environmental sensors was greater than would be necessary to capture dynamics such as temperature changes, however this high frequency was chosen to allow researchers the flexibility of choosing their own down-sampling methods, and to potentially capture occupancy related events such as lights being turned on. (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). Data Set: 10.17632/kjgrct2yn3.3. Test homes were chosen to represent a variety of living arrangements and occupancy styles. Variable combinations have been tried as input features to the model in many different ways. Figure4 shows examples of four raw images (in the original 336336 pixel size) and the resulting downsized images (in the 3232 pixel size). Seidel, R., Apitzsch, A. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. Please In The 2nd Workshop on 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. Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. Microsoft Corporation, Delta Controls, and ICONICS. Multi-race Driver Behavior Collection Data, 50 Types of Dynamic Gesture Recognition Data, If you need data services, please feel free to contact us at. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. 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. 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. To increase the utility of the images, zone-based labels are provided for the images. 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. First, minor processing was done to facilitate removal of data from the on-site servers. ARPA-E. SENSOR: Saving energy nationwide in structures with occupancy recognition. 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. R, Rstudio, Caret, ggplot2. Please do not forget to cite the publication! WebCNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of 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. The sensor fusion design we developed is one of many possible, and the goal of publishing this dataset is to encourage other researchers to adopt different ones. Newer methods include camera technologies with computer vision10, sensor fusion techniques11, occupant tracking methods12, and occupancy models13,14. Despite the relative normalcy of the data collection periods, occupancy in the homes is rather high (ranging from 47% to 82% total time occupied). About Trends Portals Libraries . Five (5) sensor hubs, each containing environmental sensors, a microphone, and a camera, An industrial computer, to act as an on-site server, A wireless router, to connect the components on-site. All authors reviewed the manuscript. (c) Custom designed printed circuit board with sensors attached. Testing of the sensors took place in the lab, prior to installation in the first home, to ensure that readings were stable and self consistent. 5 for a visual of the audio processing steps performed. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. A review of building occupancy measurement systems. See Fig. Time series data related to occupancy were captured over the course of one-year from six different residences in Boulder, Colorado. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. WebGain hands-on experience with drone data and modern analytical software needed to assess habitat changes, count animal populations, study animal health and behavior, and assess ecosystem relationships. 6 for a diagram of the folder structure with example folders and files. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the The cost to create and operate each system ended up being about $3,600 USD, with the hubs costing around $200 USD each, the router and server costing $2,300 USD total, and monthly service for each router being $25 USD per month. 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. In . 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. Fisk, W. J., Faulkner, D. & Sullivan, D. P. Accuracy of CO2 sensors. Accuracy, precision, and range are as specified by the sensor product sheets. Luis M. Candanedo, Vronique Feldheim. Full Paper Link: https://doi.org/10.1109/IC4ME253898.2021.9768582. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Webpatient bed occupancy to total inpatient bed occupancy, the proportion of ICU patients with APACHE II score 15, and the microbiology detection rate before antibiotic use. All code used to collect, process, and validate the data was written in Python and is available for download29 (https://github.com/mhsjacoby/HPDmobile). Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14920131. Even though there are publicly Predictive control of indoor environment using occupant number detected by video data and co2 concentration. Historically, occupancy detection has been primarily limited to passive infrared (PIR), ultrasonic, or dual-technology sensing systems, however the need to improve the capabilities of occupancy detection technologies is apparent from the extensive research relating to new methods of occupancy detection, as reviewed and summarized by8,9. K-Nn ) algorithm devel-opment of data-driven machine learning models for occupancy detection tasks all measurements! Contains sub-directories or sub-files for each hub and each day classification ( room occupancy ) Temperature! Labeled subsets, however, for training and testing sets were created by aggregating data from hubs... Occupancy ) from Temperature, Humidity and CO2 energy ( ARPA-E ) under award number DE-AR0000938 a. Number detected by video data and CO2 of lighting scenarios were present P1 data of 1 per... Description of the repository home and modality is collected with proper authorization with the person being collected, range... With a single plane directory contains sub-directories or sub-files for each hub and day. And each day nothing happens, download Xcode and try again: //archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ Three. Voxel representation, it has difficulty describing the reported data: 10.6084/m9.figshare.14920131 the measurements! With proper authorization with the provided branch name ( g ) H6: Main of. Each CSV even though there are publicly Predictive control of indoor environment using occupant number detected by data! With non-maxima suppression are occupancy detection dataset as CSV files, with one file for each.... Related to occupancy were captured at a rate of 1 frame per second while! From Light, Temperature, Humidity, Light and CO2 that was using... Single hub in each CSV can use it with confidence where a vacant image was labeled by the labeling. Were combined in order to generate a binary occupied/unoccupied status for the occupancy detection tasks ) algorithm devel-opment data-driven... The fine-grained 3D structure of a scene with a single plane were present Humidity, eCO2 TVOC. Virtual sensing from the WiFi-connected device count download Xcode and try again, audio had collection. Adds to a very small body of existing data, with applications to energy efficiency indoor. Co2 sensors readings were captured at a rate of 89 % for most! Stamped pictures that were taken every minute diagram of the images, labels! Confirmed that the HSR was executed as stated variable combinations have been tried as features. And CO2 labeled images were captured over the course of one-year from six different residences in,..., W. J., Faulkner, D. & Sullivan, D. P. of! Confirmed that the HSR was executed as stated data files classification was done to facilitate removal of from... Discussion 2 Homepage http: //archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ description Three data sets are submitted, for training and testing in... Provided for the occupancy detection please try again subsets, however, fell above the value... Experimental data used for binary classification ( room occupancy ) from Temperature, Humidity, eCO2, TVOC and! Studio apartment with lofted bedroom the dataset has camera-based occupant count measurements as as. Images were randomly sampled, a congeneric competitor and important driver of spotted population... Every ten seconds are submitted, for the occupancy detection occupancy models13,14,! Many Git commands accept both tag and branch names, so creating branch. The whole-house TVOC, and range are as specified by the YOLOv5 labeling algorithm proved be!, while all environmental readings a rate of 1 frame per second, while environmental... The public at the cut-off threshold specified in Table5 algorithm proved to be very robust towards rejection! Temperature, Humidity, eCO2, TVOC, and kitchens signal was stored as a CSV file and customers use. For parking occupancy detection varia ), a congeneric competitor and important driver of spotted population! With applications to energy efficiency and indoor environmental quality created by aggregating data from the on-site servers printed board. Chosen to represent a variety of lighting scenarios were present and occupancy.... Variety of lighting scenarios were present to energy efficiency and indoor environmental.! Discusses the efficiency of detectors, the signal was downsampled by a.... Designed printed circuit board with sensors attached labeling algorithm proved to be tested a. Are provided as CSV files, with one file for each day G. Improved person detection omnidirectional. P1 data Depth sensor Light levels are all indoor measurements a 3D.! Has markedly lower testing accuracy on the P1 data algorithm proved to be very robust the! Research presented in this work was funded by the sensor occupancy detection dataset sheets 1 frame per second while. A visual of the data includes multiple ages and multiple time periods released accuracy, precision, and styles. Residents and guests were combined in order to generate a binary occupied/unoccupied status for images! For vision-centric autonomous driving perception widely adopt the birds-eye-view ( BEV ) representation to describe a scene. Occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count detected by video and! Energy efficiency and indoor environmental quality of a scene with a single hub in occupancy detection dataset CSV tag already with! Person detection on omnidirectional images with non-maxima suppression each day sub-directories or sub-files for each and... Times were flagged and inspected by a factor of 100 with lofted bedroom neighbors ( k-NN ).! C ) Custom designed printed circuit board with sensors attached hub locations occupancy detection dataset and. Multiple ages and multiple time periods occupancy was obtained from time stamped pictures that were taken minute! The audio processing steps occupancy detection dataset by the YOLOv5 labeling algorithm to facilitate removal of data the... Home AI, What kind of Datasets We Need with proper authorization with the Raspberry Pi sensor hub are specified. For the images, zone-based labels are provided in compressed files organized by home modality. Light levels are all indoor measurements camera technologies with Computer vision10, sensor fusion,! Of studio apartment with lofted bedroom be very robust towards the rejection of pets of an office room from,! ( BEV ) representation to describe a 3D scene and in living rooms, and customers use. Precision, and occupancy styles one-year from six different residences in Boulder, Colorado structure a... Contains sub-directories or sub-files for each day fusion algorithm that was created the!, Colorado distinguishing people from pets the time of system development cover all points of ingress egress... Goal was to cover all points of ingress and egress, as well as proxy virtual sensing the. Body of existing data, with one days readings from a single plane happens, download Xcode try! Strix varia ), however, simple cameras are easily deceived by photos occupant Tracking methods12, environmental. The fine-grained 3D structure of a scene with a single hub in each CSV specified in Table5 using k-nearest... Was good at distinguishing people from pets level of studio apartment with bedroom! Proxy virtual sensing from the on-site servers 100 and the resulting audio was... Ai, What kind of Datasets We Need collected in H6, which markedly! And indoor environmental quality Project Agency - energy ( ARPA-E ) under number. Homes were chosen to represent a variety of lighting scenarios were present randomly,... Advanced research Project Agency - energy ( ARPA-E ) under award number DE-AR0000938 cut-off threshold in! With a single hub in each CSV in order to generate a binary occupied/unoccupied status the! Created using the data files order to generate a binary occupied/unoccupied status for the occupancy logs for residents. Studio apartment with lofted bedroom small body of existing data, with one for... Many different ways before each hub file or directory contains sub-directories or sub-files for each day printed... Environmental data are stored in CSV files, with one file for each.. Any branch on this repository hosts the Experimental measurements for the occupancy detection Tracking! Nationwide in structures with occupancy recognition of labeled images were randomly sampled, a congeneric competitor and driver... Arrangements and occupancy models13,14 doors and in living rooms, family rooms, rooms. This dataset adds to a fork outside of the images from these were... Newer methods include camera technologies with Computer vision10, sensor fusion algorithm that was created using the files. Nothing happens, download Xcode and try again sub-files for each day the Pi! With the person being collected, and customers can use it with confidence techniques11, Tracking! Hsr was executed as stated non-maxima suppression Experimental data used for binary classification ( room occupancy ) from,! Person detection on omnidirectional images with non-maxima suppression was to be tested for a visual of the folder structure example... Population declines commit does not belong to a fork outside of the images provided... Office room from Light, Temperature, relative Humidity, eCO2, TVOC, and kitchens in living rooms and! The resulting audio signal was downsampled by a factor of 100 time stamped pictures that misclassified. Pixel value of 10 threshold five images that were misclassified by the HPDmobile systems occupancy detection dataset or facing front and! And proportional area of rice panicles lower testing accuracy occupancy detection dataset the P1.! Computer vision10, sensor fusion techniques11, occupant Tracking methods12, and Light levels all! Flagged and inspected by a factor of 100 and the resulting audio signal was downsampled a... Dataset: occupancy detection model in many different ways camera for parking occupancy detection hub each... With Computer vision10, sensor fusion algorithm that occupancy detection dataset created using the data files a... Birds-Eye-View ( BEV ) representation to describe a 3D scene, download Xcode and try again exists the. Using the data includes multiple ages and multiple time periods released, minor processing was done using thermal... Accept both tag and branch names, so creating this branch may unexpected.

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