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. Energy and Buildings. In most cases, sensor accuracy was traded in favor of system cost and ease of deployment, which led to less reliable environmental measurements. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. 2019. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. Web[4], a dataset for parking lot occupancy detection. 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. Opportunistic occupancy-count estimation using sensor fusion: A case study. 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 final data that has been made public was chosen so as to maximize the amount of available data in continuous time-periods. The homes included a single occupancy studio apartment, individuals and couples in one and two bedroom apartments, and families and roommates in three bedroom apartments and single-family houses. Seidel, R., Apitzsch, A. 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). / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). All image processing was done with the Python Image Library package (PIL)30 Image module, version 7.2.0. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. Area monitored is the estimated percent of the total home area that was covered by the sensors. This operated through an if-this-then-that (IFTTT) software application that was installed on a users cellular phone. Multi-race Driver Behavior Collection Data. Abstract: Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. The 2022 perception and prediction challenges are now closed, but the leaderboards remain open for submissions. Because of size constraints, the images are organized with one hub per compressed file, while the other modalities contain all hubs in one compressed file. R, Rstudio, Caret, ggplot2. Reliability of the environmental data collection rate (system performance) was fairly good, with higher than 95% capture rate for most modalities. WebOccupancy-detection-data. 10 for 24-hour samples of environmental data, along with occupancy. Ground-truth occupancy was Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. 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. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. From these verified samples, we generated point estimates for: the probability of a truly occupied image being correctly identified (the sensitivity or true positive rate); the probability of a truly vacant image being correctly identified (the specificity or true negative rate); the probability of an image labeled as occupied being actually occupied (the positive predictive value or PPV); and the probability of an image labeled as vacant being actually vacant (the negative predictive value or NPV). Luis M. Candanedo, Vronique Feldheim. See Technical Validation for results of experiments comparing the inferential value of raw and processed audio and images. (b) Waveform after applying a mean shift. 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. All Rights Reserved. This repository has been archived by the owner on Jun 6, 2022. Please cite the following publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Webusetemperature,motionandsounddata(datasets are not public). In terms of device, binocular cameras of RGB and infrared channels were applied. The images from these times were flagged and inspected by a researcher. Are you sure you want to create this branch? Figure4 shows examples of four raw images (in the original 336336 pixel size) and the resulting downsized images (in the 3232 pixel size). 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. Data for each home consists of audio, images, environmental modalities, and ground truth occupancy information, as well as lists of the dark images not included in the dataset. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. WebThe field of machine learning is changing rapidly. 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. Contact us if you The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. See Table6 for sensor model specifics. 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. Careers, Unable to load your collection due to an error. Despite the relative normalcy of the data collection periods, occupancy in the homes is rather high (ranging from 47% to 82% total time occupied). In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. Use Git or checkout with SVN using the web URL. The temperature and humidity sensor had more dropped points than the other environmental modalities, and the capture rate for this sensor was around 90%. Luis M. Candanedo, Vronique Feldheim. Data that are captured on the sensor hub are periodically transmitted wirelessly to the accompanying VM, where they are stored for the duration of the testing period in that home. See Table3 for a summary of the collection reliability, as broken down by modality, hub, and home. WebData Descriptor occupancy detection dataset Margarite Jacoby 1 , Sin Yong Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. Thus, data collection proceeded for up to eight weeks in some of the homes. Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. Missing data are represented as blank, unfilled cells in the CSVs. 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. A tag already exists with the provided branch name. Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. Using environmental sensors to collect data for detecting the occupancy state See Fig. WebOccupancy Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. to use Codespaces. Five images that were misclassified by the YOLOv5 labeling algorithm. The development of a suitable sensor fusion technique required significant effort in the context of this project, and the final algorithm utilizes isolation forests, convolutional neural networks, and spatiotemporal pattern networks for inferring occupancy based on the individual modalities. Timestamp data are omitted from this study in order to maintain the model's time independence. Learn more. WebAbstract. The batteries also help enable the set-up of the system, as placement of sensor hubs can be determined by monitoring the camera output before power-cords are connected. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. Occupancy detection in buildings is an important strategy to reduce overall energy consumption. Sun K, Zhao Q, Zou J. Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. 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 released dataset is hosted on figshare25. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Data Set Information: Three data sets are submitted, for training and testing. Are you sure you want to create this branch? In some cases this led to higher thresholds for occupancy being chosen in the cross-validation process, which led to lower specificity, along with lower PPV. 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. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. Summary of the completeness of data collected in each home. See Table4 for classification performance on the two file types. These include the seat belt warning function, judging whether the passengers in the car are seated safely, whether there are children or pets left alone, whether the passengers are wearing seat belts, etc. Luis M. Candanedo, Vronique Feldheim. Accuracy, precision, and range are as specified by the sensor product sheets. ARPA-E. SENSOR: Saving energy nationwide in structures with occupancy recognition. 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. Test homes were chosen to represent a variety of living arrangements and occupancy styles. 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. 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. 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. (c) Custom designed printed circuit board with sensors attached. The two homes with just one occupant had the lowest occupancy rates, since there were no overlapping schedules in these cases. False positive cases, (i.e., when the classifier thinks someone is in the image but the ground truth says the home is vacant) may represent a mislabeled point. Yang J, Santamouris M, Lee SE. (a) Raw waveform sampled at 8kHz. 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. In addition, zone-labels are provided for images, which indicate with a binary flag whether each image shows a person or not. WebAbout Dataset Data Set Information: The experimental testbed for occupancy estimation was deployed in a 6m 4.6m room. Description Three data sets are submitted, for training and testing. An official website of the United States government. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. 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