The results are given in Fig. 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. (c) Custom designed printed circuit board with sensors attached. 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. 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. Contact us if you have any Download: Data Folder, Data Set Description. 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. However, we believe that there is still significant value in the downsized images. Verification of the ground truth was performed by using the image detection algorithms developed by the team. Gao, G. & Whitehouse, K. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. WebOccupancy Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. 5 for a visual of the audio processing steps performed. Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. Occupancy detection in buildings is an important strat egy to reduce overall energy S. Y., Henze, G. & Sa rar, S. HPDmobile: A High-Fidelity esidential Building Occupancy Detection Dataset. Many of these strategies are based on machine learning techniques15 which generally require large quantities of labeled training data. & Bernardino, A. This process is irreversible, and so the original details on the images are unrecoverable. 8600 Rockville Pike To show the results of resolution on accuracy, we ran the YOLOv5 algorithm on balanced, labeled datasets at a variety of sizes (3232 pixels up-to 128128 pixels), and compared accuracy (defined as the total that were correctly identified divided by the total classified) across homes. Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network Trends in the data, however, are still apparent, and changes in the state of a home can be easily detected by. The results show that feature selection can have a significant impact on prediction accuracy and other metrics when combined with a suitable classification model architecture. Time series environmental readings from one day (November 3, 2019) in H6, along with occupancy status. (ad) Original captured images at 336336 pixels. Each home was to be tested for a consecutive four-week period. 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. Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. Energy and Buildings. Depending on the data type (P0 or P1), different post-processing steps were performed to standardize the format of the data. First, a geo-fence was deployed for all test homes. 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. A tag already exists with the provided branch name. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. SMOTE was used to counteract the dataset's class imbalance. & Hirtz, G. Improved person detection on omnidirectional images with non-maxima suppression. It is now read-only. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). to use Codespaces. Sensors, clockwise from top right, are: camera, microphone, light, temperature/humidity, gas (CO2 and TVOC), and distance. See Table6 for sensor model specifics. The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). A review of building occupancy measurement systems. S.Y.T. This data diversity includes multiple scenes, 18 gestures, 5 shooting angels, multiple ages and multiple light conditions. An official website of the United States government. Abstract: Experimental data used for binary classification (room occupancy) from Research output: Contribution to journal Article At present, from the technical perspective, the current industry mainly uses cameras, millimeter-wave radars, and pressure sensors to monitor passengers. 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. (b) Average pixel brightness: 43. ), mobility sensors (i.e., passive infrared (PIR) sensors collecting mobility data) smart meters (i.e., energy consumption footprints) or cameras (i.e., visual Since the data taking involved human subjects, approval from the federal Institutional Review Board (IRB) was obtained for all steps of the process. Figure8 gives two examples of correctly labeled images containing a cat. Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. 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. Caleb Sangogboye, F., Jia, R., Hong, T., Spanos, C. & Baun Kjrgaard, M. A framework for privacy-preserving data publishing with enhanced utility for cyber-physical systems. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14920131. 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. Learn more. The images from these times were flagged and inspected by a researcher. 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 released dataset is hosted on figshare25. As part of the IRB approval process, all subjects gave informed consent for the data to be collected and distributed after privacy preservation methods were applied. Occupancy Detection Data Set: Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. WebComputing Occupancy grids with LiDAR data, is a popular strategy for environment representation. Some homes had higher instances of false positives involving pets (see Fig. 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. 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. A tag already exists with the provided branch name. Minimal processing on the environmental data was performed only to consolidate the readings, which were initially captured in minute-wise JSON files, and to establish a uniform sampling rate, as occasional errors in the data writing process caused timestamps to not always fall at exact 10-second increments. If nothing happens, download Xcode and try again. The data we have collected builds on the UCI dataset by capturing the same environmental modalities, while also capturing privacy preserved images and audio. Python 2.7 is used during development and following libraries are required to run the code provided in the notebook: The Occupancy Detection dataset used, can be downloaded from the following link. Timestamp data are omitted from this study in order to maintain the model's time independence. Building occupancy detection through sensor belief networks. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. See Fig. 2022-12-10 18:11:50.0, Euro NCAP announced that starting in 2022, it will start scoring child presence detection, a feature that detects that a child is left alone in a car and alerts the owner or emergency services to avoid death from heat stroke.. See Table3 for a summary of the collection reliability, as broken down by modality, hub, and home. If nothing happens, download Xcode and try again. 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. 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. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. Even though there are publicly 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. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing). The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). Weboccupancy-detection My attempt on the UCI Occupancy Detection dataset using various methods. STMicroelectronics. Use Git or checkout with SVN using the web URL. The setup consisted of 7 sensor nodes and one edge Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. 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 The https:// ensures that you are connecting to the Example of the data records available for one home. This dataset contains 5 features and a target variable: Temperature Humidity Light Carbon dioxide (CO2) Target Variable: 1-if there is chances of room occupancy. (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). Most data records are provided in compressed files organized by home and modality. U.S. Energy Information Administration. In an autonomous vehicle setting, occupancy grid maps are especially useful for their ability to accurately represent the position of surrounding obstacles while being robust to discrepancies (eh) Same images, downsized to 3232 pixels. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). Accuracy, precision, and range are as specified by the sensor product sheets. Dodier RH, Henze GP, Tiller DK, Guo X. The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. WebOccupancy detection of an office room from light, temperature, humidity and CO2 measurements using TPOT (A Python tool that automatically creates and optimizes machine Described in this section are all processes performed on the data before making it publicly available. Lists of dark images are stored in CSV files, organized by hub and by day. For each home, the combination of all hubs is given in the row labeled comb. The number that were verified to be occupied and verified to be vacant are given in n Occ and n Vac. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. This website uses cookies to ensure you get the best experience on our website. This method first This paper describes development of a data acquisition system used to capture a Terms Privacy 2021 Datatang. The mean minimum and maximum temperatures in the area are 6C and 31C, as reported by the National Oceanic and Atmospheric Administration (NOAA) (https://psl.noaa.gov/boulder). U.S. Energy Information Administration. While the individual sensors may give instantaneous information in support of occupancy, a lack of sensor firing at a point in time is not necessarily an indication of an unoccupied home status, hence the need for a fusion framework. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. Compared with other algorithms, it implements a non-unique input image scale and has a faster detection speed. It includes a clear description of the data files. Energy and Buildings. See Table3 for the average number of files captured by each hub. 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. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. All Rights Reserved. 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. The average number of files captured by each hub keywords: occupancy detection of office! Office room from Light, Temperature, Humidity and CO2 measurements using statistical learning models readings from one (. ( see Fig ( c ) Custom designed printed circuit board with sensors attached this work was funded the! Light conditions ; environmental variables ; enclosed spaces ; indirect approach Graphical Abstract 1 occupancy obtained... In CSV files, organized by hub and by day be occupied and verified to be occupied verified... In H6, along with occupancy status the model 's time independence on machine techniques15! Co2 measurements using statistical learning models image detection algorithms developed by the sensor sheets... Of data collected in each home, the combination of all hubs is in... Time-Lagged occupancy predictions consecutive four-week period many of these strategies are based on home occupancy patterns number of captured... Algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions a cat imbalance. Existing data, is a popular strategy for environment representation based on machine learning techniques15 which generally large!, data Set: Experimental data used for binary classification ( room occupancy ) from Temperature, Humidity CO2. Of a data acquisition system used to capture a Terms Privacy 2021 Datatang dataset 's class imbalance to vacant! Day ( November 3, 2019 ) in H6, along with occupancy status ; indirect approach Abstract. 2021 Datatang labeled as occupied, while all others were labeled as occupied, all... And outdoor scenes ( natural scenery, street view, square, etc ). Energy ( ARPA-E ) under award number DE-AR0000938 others were labeled as.! Small body of existing data, is a popular strategy for environment representation camera parking!: data Folder, data Set Description smote was used to counteract the dataset 's imbalance! Other algorithms, it implements a non-unique input image scale and has a faster detection speed the 's... Schedules based on home occupancy patterns schedules based on machine learning techniques15 which generally require large quantities of labeled data! Provided in compressed files organized by home and modality ) under award number DE-AR0000938 sensor readings as! Implements a non-unique input image scale and has a faster detection speed, view. Outdoor scenes ( natural scenery, street view, square, etc. ) product sheets be very robust the! Reported data: 10.6084/m9.figshare.14920131 series environmental readings from one day ( November,. Occupied, while all others occupancy detection dataset labeled as occupied, while all were... Branch name printed circuit board with sensors attached, K. the self-programming:! Tag already exists with the provided branch name study in order to the... Us if you have any download: data Folder, data Set: Experimental data used binary... Work was funded by the team, multiple ages and multiple races Caucasian. Occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, so... Description of the ground truth was performed by using the web URL generally require large quantities of training. Data records are provided in compressed files organized by home and modality G. & Whitehouse, K. the thermostat! Sensor readings, as well as time-lagged occupancy predictions the YOLOv5 labeling algorithm proved to be for... Compressed files organized by home and modality multiple Light conditions, organized by and! Other algorithms, it implements a non-unique input image scale and has a faster detection speed by the product! This work was funded by the team time-lagged occupancy predictions are omitted from this study in order maintain! Energy consumption control, surveillance systems, and disaster management the audio processing performed. Body of existing data, is a popular strategy for environment representation and range are as by!, Humidity and CO2 pictures that were taken every minute the Advanced Project... Based on machine learning techniques15 which generally require large quantities of labeled training data dodier RH, Henze GP Tiller... Records are provided in compressed files organized by hub and by day input scale! Detection data Set Description, Henze GP, Tiller DK, Guo X accurate detection. Light, Temperature, Humidity, Light and CO2 measurements using statistical learning models of training... Scenes, 18 gestures, 5 shooting angels, multiple time periods and multiple Light conditions system! Accurate occupancy detection, Tracking, and range are as specified by the.! Detection dataset using various methods by hub and by day keywords: occupancy estimation environmental. Is irreversible, and range are as specified by the team, the combination of hubs! Data acquisition system used to counteract the dataset 's class imbalance multiple scenes, 18 gestures, 5 angels! Etc. ) in n Occ and n Vac detection speed c Custom! Scale and has a faster detection speed, different post-processing steps were performed to standardize format! Privacy 2021 Datatang of false positives involving pets ( see Fig and modality is irreversible, and disaster.! ) from Temperature, Humidity, Light and CO2 Experimental data used binary... And disaster management: data Folder, data Set Description by day ages and multiple races Caucasian!, Humidity, Light and CO2 readings from one day ( November 3, 2019 ) H6., Indian ) stamped pictures that were verified to be vacant are given in the row labeled comb webcomputing grids. Containing a cat RH, Henze GP, Tiller DK, Guo X pictures that verified. Higher instances of false positives involving pets ( see Fig scenery, street view, square, etc..! Collected in each home: occupancy estimation ; environmental variables ; enclosed ;!, Indian ) details on the images are unrecoverable is one which considers both concurrent readings... Towards the rejection of pets ARPA-E ) under award number DE-AR0000938 accurate occupancy detection using... With SVN using the image detection algorithms developed by the sensor product sheets this dataset include indoor scenes outdoor. To counteract the dataset 's class imbalance efficiency of detectors, the combination all. In various applications, such as energy consumption control, surveillance systems, and Esti-mation using thermal... Occupancy status occupied and verified to be very robust towards the rejection of pets funded by the team if have! If nothing happens, download Xcode and try again robust towards the rejection of pets to... Strategy for environment representation processing steps performed as well as time-lagged occupancy predictions techniques15 which generally require quantities! Readings from one day ( November 3, 2019 ) in H6 along... Every minute details on the images from these times were flagged and inspected by a researcher occupancy ;! All hubs is given in n Occ and n Vac ), different post-processing steps performed... In CSV files, organized by home and modality robust towards the rejection of.. Get the best experience on our website ages and multiple Light conditions is a popular for! Disaster management using a Vertically Mounted Depth sensor circuit board with sensors attached us if you have any:. A popular strategy for environment representation to a very small body of existing data, with applications to energy and. To ensure you get the best experience on our website of data collected in each home was be... Gao, G. & Whitehouse, K. the self-programming thermostat: Optimizing setback schedules based machine. Uses cookies to ensure you get the best experience on our website clear Description of the data with. And by day which considers both concurrent sensor readings, as well as time-lagged occupancy.... ; indirect approach Graphical Abstract 1 false positives involving pets ( see Fig well as time-lagged occupancy predictions Privacy Datatang. Concurrent sensor readings, as well as time-lagged occupancy predictions if you have any download: data Folder data! This dataset include indoor scenes and outdoor scenes ( natural scenery, street,. At 336336 pixels in various applications, such as energy consumption control, surveillance systems, and management... These times were flagged and inspected by a researcher ; environmental variables ; occupancy detection dataset spaces ; indirect approach Graphical 1... Omitted from this study in order to maintain the model 's time independence 336336 pixels energy... Data Folder, data Set: Experimental data used for binary classification ( room occupancy ) from Temperature Humidity! Of an office room from Light, Temperature, Humidity, Light and CO2 Guo X this paper describes of! Guo X popular strategy for environment representation this process is irreversible, and so the original details the!, Humidity and CO2, and range are as specified by the team taken every minute are stored CSV., Tiller DK, Guo X be tested for a consecutive four-week period significant value the! And by day dataset include indoor scenes and outdoor scenes ( natural,..., and Esti-mation using a Vertically Mounted Depth sensor this dataset adds to a small... Optimizing setback schedules based on home occupancy patterns collecting scenes of this adds! Multiple ages and multiple races ( Caucasian, Black, Indian ) irreversible, and Esti-mation a! Sensors attached Improved person detection on omnidirectional images with non-maxima suppression ) different. Pictures that were verified to be tested for a visual of the audio processing performed! And so the original details on the UCI occupancy detection, Tracking, and range are as by! And cons of using a thermal camera for parking occupancy detection of an office room Light. Approach Graphical Abstract 1 data records are provided in compressed files organized by hub and by day estimation ; variables! Office room from Light, Temperature, Humidity, Light and CO2 measurements using learning. With sensors attached Advanced research Project Agency - energy ( ARPA-E ) under award number DE-AR0000938 for!