All code used to collect, process, and validate the data was written in Python and is available for download29 (https://github.com/mhsjacoby/HPDmobile). 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. (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. 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. to use Codespaces. Summary of the completeness of data collected in 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. 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. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. 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). Luis M. Candanedo, Vronique Feldheim. The Previous: Using AI-powered Robots To Help At Winter Olympics 2022. Learn more. Sensors, clockwise from top right, are: camera, microphone, light, temperature/humidity, gas (CO2 and TVOC), and distance. 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. WebExperimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. See Table6 for sensor model specifics. Overall the labeling algorithm had good performance when it came to distinguishing people from pets. The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. Data Set: 10.17632/kjgrct2yn3.3. 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. The .gov means its official. Note that the term server in this context refers to the SBC (sensor hub), and not the the on-site server mentioned above, which runs the VMs. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. All data was captured in 2019, and so do not reflect changes seen in occupancy patterns due to the COVID-19 global pandemic. (e) H4: Main level of two-level apartment. (c), (d), and (e) are examples of false positives, where the images were labeled as occupied at the thresholds used (0.5, 0.3, and 0.6, respectively). Created by university of Nottingham 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. The dataset has camera-based occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count. 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. Images had very high collection reliability, and total image capture rate was 98% for the time period released. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. About Trends Portals Libraries . WebOccupancy Detection Data Set Download: Data Folder, Data Set Description. We also quantified detections of barred owls ( Strix varia ), a congeneric competitor and important driver of spotted owl population declines. Used Dataset link: https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The Pext: Build a Smart Home AI, What kind of Datasets We Need. Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. WebAbstract. Timestamps were simply rounded to the nearest 10-second increment, and any duplicates resulting from the process were dropped. 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. Volume 112, 15 January 2016, Pages 28-39. GitHub is where people build software. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. The ECO dataset captures electricity consumption at one-second intervals. For each home, the combination of all hubs is given in the row labeled comb. The data we have collected builds on the UCI dataset by capturing the same environmental modalities, while also capturing privacy preserved images and audio. Contact us if you have any These predictions were compared to the collected ground truth data, and all false positive cases were identified. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. This repository has been archived by the owner on Jun 6, 2022. / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. Installed on the roof of the cockpit, it can sense all areas of the entire cockpit, detect targets, and perform high-precision classification and biometric monitoring of them. Ground-truth occupancy was Accuracy, precision, and range are as specified by the sensor product sheets. The on-site server was needed because of the limited storage capacity of the SBCs. (a) Raw waveform sampled at 8kHz. Web99 open source Occupancy images plus a pre-trained Occupancy model and API. Compared with other algorithms, it implements a non-unique input image scale and has a faster detection speed. Datatang 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. (d) Waveform after downsampling by integer factor of 100. Three data sets are submitted, for training and testing. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This ETHZ CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding. sign in All collection code on both the client- and server-side were written in Python to run on Linux 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. While these reductions are not feasible in all climates, as humidity or freezing risk could make running HVAC equipment a necessity during unoccupied times, moderate temperature setbacks as a result of vacancy information could still lead to some energy savings. 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). See Table1 for a summary of modalities captured and available. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. False negatives were not verified in similar fashion, as false negatives from the images (i.e., someone is home but the camera does not see them) were very common, since the systems ran 24-hours a day and people were not always in rooms that had cameras installed. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Cite this APA Author BIBTEX Harvard Standard RIS Vancouver Source: In addition, zone-labels are provided for images, which indicate with a binary flag whether each image shows a person or not. An Artificial Neural Network (ANN) was used in this article to detect room occupancy from sensor data using a simple deep learning model. 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. OMS is to further improve the safety performance of the car from the perspective of monitoring passengers. 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. To ensure accuracy, ground truth occupancy was collected in two manners. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality. 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. to use Codespaces. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. 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. A pre-trained object detection algorithm, You Only Look Once - version 5 (YOLOv5)26, was used to classify the 112112 pixel images as occupied or unoccupied. Accessibility 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). Sign In; Datasets 7,801 machine learning datasets Subscribe to the PwC Newsletter . National Library of Medicine 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. 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). An example of this is shown in Fig. Please The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally The goal was to cover all points of ingress and egress, as well as all hang-out zones. The https:// ensures that you are connecting to the All authors reviewed the manuscript. In total, three datasets were used: one for training and two for testing the models in open and closed-door occupancy scenarios. Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network Full Paper Link: https://doi.org/10.1109/IC4ME253898.2021.9768582. Howard B, Acha S, Shah N, Polak J. WebOccupancy grid maps are widely used as an environment model that allows the fusion of different range sensor technologies in real-time for robotics applications. (c) Waveform after full wave rectification. 2, 28.02.2020, p. 296-302. Virtanen P, et al. See Table4 for classification performance on the two file types. Webusetemperature,motionandsounddata(datasets are not public). Monthly energy review. 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. PeopleFinder (v2, GoVap), created by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. This is most likely due to the relative homogeneity of the test subjects, and the fact that many were graduate students with atypical schedules, at least one of whom worked from home exclusively. 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. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status. 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. Depending on the data type (P0 or P1), different post-processing steps were performed to standardize the format of the data. Interested researchers should contact the corresponding author for this data. Work fast with our official CLI. In other cases, false negatives were found to occur more often in cameras that had a long field of view, where people spent time far from the camera. Microsoft Corporation, Delta Controls, and ICONICS. In light of recently introduced systems, such as Delta Controls O3 sensor hub24, a custom designed data acquisition system may not be necessary today. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. Audio files were captured back to back, resulting in 8,640 audio files per day. (b) Final sensor hub (attached to an external battery), as installed in the homes. The binary status reported has been verified, while the total number has not, and should be used as an estimate only. Next, processing to validate the data and check for completeness was performed. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. 9. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. Energy and Buildings. 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. 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. 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. WebKe et al. This is a repository for data for the publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 G.H. 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. First, a geo-fence was deployed for all test homes. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. M.J. created the data acquisition system, performed all data collection tasks, processed and validated the collected data, and wrote the manuscript. This is a repository for data for the publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). 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. Data Set License: CC BY 4.0. WebThis is the dataset Occupancy Detection Data Set, UCI as used in the article how-to-predict-room-occupancy-based-on-environmental-factors Content This repository hosts the experimental measurements for the occupancy detection tasks. 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. Thank you! Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. 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 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. Additional key requirements of the system were that it (3) have the ability to collect data concurrently from multiple locations inside a house, (4) be inexpensive, and (5) operate independently from residential WiFi networks. Please read the commented lines in the model development file. Learn more. HHS Vulnerability Disclosure, Help Seidel, R., Apitzsch, A. Thus new pixel values are generated from linear combinations of the original values. Jacoby M, Tan SY, Henze G, Sarkar S. 2021. Using AI-powered Robots to Help At Winter Olympics 2022 has camera-based occupant count measurements as well as proxy sensing. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms and.... And check for completeness was performed because of the living space Graphical Abstract.. Identified through conversations with the person being collected, and should be used as an estimate only which both... 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