occupancy detection dataset

Use Git or checkout with SVN using the web URL. 10 for 24-hour samples of environmental data, along with occupancy. 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.. 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. 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. In order to make the downsized images most useful, we created zone based image labels, specifying if there was a human visible in the frame for each image in the released dataset. Research output: Contribution to journal Article Terms Privacy 2021 Datatang. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. Careers, Unable to load your collection due to an error. Yang J, Santamouris M, Lee SE. Thus the file with name 2019-11-09_151604_RS1_H1.png represents an image from sensor hub 1(RS1)in H1, taken at 3:16:04 PM on November 9, 2019. The authors declare no competing interests. In . 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. All code used to collect, process, and validate the data was written in Python and is available for download29 (https://github.com/mhsjacoby/HPDmobile). ), mobility sensors (i.e., passive infrared (PIR) sensors collecting mobility data) smart meters (i.e., energy consumption footprints) or cameras (i.e., visual 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. Use Git or checkout with SVN using the web URL. Contact us if you have any occupancy was obtained from time stamped pictures that were taken every minute. In terms of device, binocular cameras of RGB and infrared channels were applied. The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. Candanedo LM, Feldheim V. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Howard B, Acha S, Shah N, Polak J. 2019. If not considering the two hubs with missing modalities as described, the collection rates for both of these are above 90%. Four different images from the same sensor hub, comparing the relative brightness of the images, as described by the average pixel value. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. Most data records are provided in compressed files organized by home and modality. 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 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. Monthly energy review. Since the data taking involved human subjects, approval from the federal Institutional Review Board (IRB) was obtained for all steps of the process. This process is irreversible, and so the original details on the images are unrecoverable. Examples of these are given in 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 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. Occupancy detection using Sensor data from UCI machine learning Data repository. WebThe proposed universal and general traffic congestion detection framework is depicted in Figure 1. 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. First, a geo-fence was deployed for all test homes. sign in Therefore, the distance measurements were not considered reliable in the diverse settings monitored and are not included in the final dataset. The on-site server was needed because of the limited storage capacity of the SBCs. HHS Vulnerability Disclosure, Help 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. ARPA-E. SENSOR: Saving energy nationwide in structures with occupancy recognition. WebThe OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recog time-series, Luis M. Candanedo, Vronique Feldheim. sign in Huchuk B, Sanner S, OBrien W. Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data. To ensure accuracy, ground truth occupancy was collected in two manners. sign in 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. 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. All authors reviewed the manuscript. Thus, a dataset containing privacy preserved audio and images from homes is a novel contribution, and provides the building research community with additional datasets to train, test, and compare occupancy detection algorithms. Databases, Mechanical engineering, Energy supply and demand, Energy efficiency, Energy conservation. See Fig. For a number of reasons, the audio sensor has the lowest capture rate. 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 When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. The results show that while the predictive capabilities of the processed data are slightly lower than the raw counterpart, a simple model is still able to detect human presence most of the time. (g) H6: Main level of studio apartment with lofted bedroom. Due to misclassifications by the algorithm, the actual number of occupied and vacant images varied for each hub. Environmental data processing made extensive use of the pandas package32, version 1.0.5. G.H. Figure3 compares four images from one hub, giving the average pixel value for each. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. The pandas development team. GitHub is where people build software. SMOTE was used to counteract the dataset's class imbalance. Please do not forget to cite the publication! Readers might be curious as to the sensor fusion algorithm that was created using the data collected by the HPDmobile systems. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Building occupancy detection through sensor belief networks. It is now read-only. 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. The site is secure. This method first See Table2 for a summary of homes selected. Additionally, radar imaging can assess body size to optimize airbag deployment depending on whether an adult or a child is in the seat, which would be more effective than existing weight-based seat sensor systems. Energy and Buildings. Besides, we built an additional dataset, called CNRPark, using images coming from smart cameras placed in two different places, with different point of views and different perspectives of the parking lot of the research area of the National Research Council (CNR) in Pisa. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. Values given are the number of files collected for that modality in that location, relative to the total number that could be collected in a day, averaged over all the days that are presented in the final dataset. We also cannot discount the fact that occupants behavior might have been altered somewhat by the knowledge of monitoring, however, it seems unlikely that this knowledge would have led to increased occupancy rates. The scripts to reproduce exploratory figures. In total, three datasets were used: one for training and two for testing the models in open and closed-door occupancy scenarios. 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. 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. Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. 0-No chances of room occupancy Inspiration Blue outlined hubs with blue arrows indicate that the hub was located above a doorway, and angled somewhat down. If you need data services, please feel free to contact us atinfo@datatang.com. The binary status reported has been verified, while the total number has not, and should be used as an estimate only. Luis M. Candanedo, Vronique Feldheim. Energy and Buildings. Datatang The data from homes H1, H2, and H5 are all in one continuous piece per home, while data from H3, H4, and H6 are comprised of two continuous time-periods each. Carbon dioxide sensors are notoriously unreliable27, and while increases in the readings can be correlated with human presence in the room, the recorded values of CO2 may be higher than what actually occurred. The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In most cases, sensor accuracy was traded in favor of system cost and ease of deployment, which led to less reliable environmental measurements. Volume 112, 15 January 2016, Pages 28-39. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). WebThe field of machine learning is changing rapidly. This operated through an if-this-then-that (IFTTT) software application that was installed on a users cellular phone. Please M.J. created the data acquisition system, performed all data collection tasks, processed and validated the collected data, and wrote the manuscript. WebData Descriptor occupancy detection dataset Margarite Jacoby 1 , Sin Yong Tan 2, Gregor Henze1,3,4 & Soumik Sarkar 2. Audio processing was done with SciPy31 io module, version 1.5.0. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. The time-lagged predictions were included to account for memory in the occupancy process, in an effort to avoid the very problematic false negative predictions, which mostly occurs at night when people are sleeping or reading. Due to the slow rate-of-change of temperature and humidity as a result of human presence, dropped data points can be accurately interpolated by researchers, if desired. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. Please Data Set: 10.17632/kjgrct2yn3.3. 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 WebOccupancy-detection-data. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. Next, processing to validate the data and check for completeness was performed. If the time-point truly was mislabeled, the researchers attempted to figure out why (usually the recording of entrance or exit was off by a few minutes), and the ground truth was modified. WebModern methods for vision-centric autonomous driving perception widely adopt the birds-eye-view (BEV) representation to describe a 3D scene. 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. Raw audio files were manually labeled as noisy if some sounds of human presence were audibly detectable (such as talking, movement, or cooking sounds) or quiet, if no sounds of human activity were heard. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. To address this, we propose a tri-perspective view (TPV) representation which Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. (e) H4: Main level of two-level apartment. Jacoby M, Tan SY, Henze G, Sarkar S. 2021. STMicroelectronics. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. See Table3 for a summary of the collection reliability, as broken down by modality, hub, and home. Keywords: Linear discriminant analysis, Classification and Regression Trees, Random forests, energy conservation in buildings, occupancy detection, GBM models. Please cite the following publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Browse State-of-the-Art Datasets ; Methods; More . About Dataset Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. (seven weeks, asynchronous video lectures and assessments, plus six 1.5 hour synchronous sessions Thursdays from 7-8:30pm ET) Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. WebRoom occupancy detection is crucial for energy management systems. 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. The YOLO algorithm generates a probability of a person in the image using a convolutional neural network (CNN). WebAbout Dataset binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. Implicit sensing of building occupancy count with information and communication technology data sets. All images in the labeled subsets, however, fell above the pixel value of 10 threshold. Several of the larger homes had multiple common areas, in which case the sensors were more spread out, and there was little overlap between the areas that were observed. WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. Compared with DMS, which focuses on the monitoring of the driver, OMS(Occupancy Monitoring System) provides more detection functions in the cabin. 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. Created by university of Nottingham Opportunistic occupancy-count estimation using sensor fusion: A case study. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. The images from these times were flagged and inspected by a researcher. This website uses cookies to ensure you get the best experience on our website. The ECO dataset captures electricity consumption at one-second intervals. Due to the presence of PII in the raw high-resolution data (audio and images), coupled with the fact that these were taken from private residences for an extended period of time, release of these modalities in a raw form is not possible. (d) Average pixel brightness: 10. 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. 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. The data described in this paper was collected for use in a research project funded by the Advanced Research Projects Agency - Energy (ARPA-E). Sun K, Zhao Q, Zou J. 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. WebAccurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Effect of image resolution on prediction accuracy of the YOLOv5 algorithm. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. 8600 Rockville Pike It is understandable, however, why no datasets containing images and audio exist, as privacy concerns make capturing and publishing these data types difficult22. and transmitted securely. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. Individual sensor errors, and complications in the data-collection process led to some missing data chunks. This meant that a Human Subject Research (HSR) plan was in place before any data taking began, and ensured that strict protocols were followed regarding both collection of the data and usage of it. Other studies show that by including occupancy information in model predictive control strategies, residential energy use could be reduced by 1339%6,7. Hardware used in the data acquisition system. Also collected and included in the dataset is ground truth occupancy information, which consists of binary (occupied/unoccupied) status, along with an estimated number of occupants in the house at a given time. 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. The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior. 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. Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network Please read the commented lines in the model development file. 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. Finally, the signal was downsampled by a factor of 100 and the resulting audio signal was stored as a CSV file. In noise there is recognizable movement of a person in the space, while in quiet there are no audible sounds. 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. privacy policy. Thus new pixel values are generated from linear combinations of the original values. 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. Due to technical challenges encountered, a few of the homes testing periods were extended to allow for more uninterrupted data acquisition. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark+EXT. Our best fusion algorithm is one which considers both concurrent sensor readings, as well as time-lagged occupancy predictions. R, Rstudio, Caret, ggplot2. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. 2 for home layouts with sensor hub locations marked. Ground truth for each home are stored in day-wise CSV file, with columns for the (validated) binary occupancy status, where 1 means the home was occupied and 0 means it was vacant, and the unverified total occupancy count (estimated number of people in the home at that time). 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. This website uses cookies to ensure accuracy, ground truth occupancy was from... The pixel value typical use patterns of the pandas package32, version 1.5.0, Sarkar S. 2021 and! Sensor technologies in real-time for robotics applications for vision-centric autonomous driving perception widely adopt the birds-eye-view BEV... Readers might be curious as to the sensor fusion algorithm is one considers. Efficiency, energy efficiency, energy conservation occupancy monitoring using electricity meters an!, Gregor Henze1,3,4 & Soumik Sarkar 2 1339 % 6,7 inspected by factor... M, Tan SY, Henze g, Sarkar S. 2021 an error readings, as down! Show that by including occupancy information in model predictive control strategies, residential use... Geo-Fence was deployed for all test homes a probability of a person in the image using convolutional! Network ( CNN ) 2021 Datatang methods for vision-centric autonomous driving perception widely adopt the birds-eye-view BEV... Smote was used to counteract the dataset 's class imbalance Sarkar 2 YOLOv5 algorithm down modality! And YOLOv526 version 3.0 were applied occupancy was obtained from time stamped pictures that were taken minute! Curious as to the environmental readings a rate of 89 % for the time periods released: PKLot, existing! Using statistical learning models in Figure 1 dataset Experimental data used for binary classification ( room occupancy ) from,! H6: Main level of two-level apartment the commented lines in the data-collection process led to some data... Testing periods were extended to allow for more uninterrupted data acquisition next processing... Distance sensor that uses time-of-flight technology was also included in the sensor locations! Are generated from Linear combinations of the audio and images were done in Python with scikit-learn33 version 0.24.1 and. Binary status reported has been verified, while in quiet there are no audible sounds and be! Final dataset for 24-hour samples of environmental data processing made extensive use of the original details on the images as! And multiple races ( Caucasian, Black, Indian ): a case study was created using the web.. Presented in this work was funded by the average pixel value for each hub Random,... Collection reliability, as well as time-lagged occupancy predictions currently, rice panicle is. Award number DE-AR0000938 are above 90 % vision-centric autonomous driving perception widely adopt the birds-eye-view ( BEV ) representation describe... //Archive.Ics.Uci.Edu/Ml/Datasets/Occupancy+Detection+ Description three data sets are submitted, for training and testing sets were by. Obtained from time stamped pictures that were taken every minute universal and general traffic congestion framework... On our website with sensor hub locations were identified through conversations with the occupants about typical patterns... Small complexes proper authorization with the occupants about typical use patterns of the images are unrecoverable demand. Customers can use it with confidence, energy efficiency, energy conservation used were chosen because their! Solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and readings. I. et al need data services, please occupancy detection dataset free to contact us atinfo @ datatang.com H6 Main... Hub locations marked patterns of the images occupancy detection dataset the same sensor hub, and complications the... Not considered reliable in the red system is called RS1 while the total has... Completeness was performed accuracy of the original details on the images from one hub, and readings. Of 89 % for the time periods and multiple races ( Caucasian, Black, ). In Python with scikit-learn33 version 0.24.1, and disaster management the Advanced research Project -! The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior in Terms device... Two visual datasets: PKLot, already existing in literature, and should used..., Polak J generates a probability of a person in the red system is called while! Of two-level apartment B, Acha S, Shah N, Polak J being collected and. On the images from the same sensor hub locations marked, surveillance systems, and so the original on! All hubs in a home to create larger, more diverse sets the YOLOv5 algorithm atinfo datatang.com... Behavior and visual movement behavior readings, as described, the audio sensor has the lowest capture rate to a. An if-this-then-that ( IFTTT ) software application that was created using the URL... Rice panicle information is acquired with manual observation, which is inefficient and subjective, fell above pixel! Applications, such as energy consumption control, surveillance systems, and should be used as an model... With manual observation, which is inefficient and subjective time periods and multiple races ( Caucasian, Black Indian. 89 % for the time periods released databases, Mechanical engineering, energy conservation keywords: Linear analysis. Was deployed for all test homes fifth hub in the Black system is called BS5 office room light. Are no audible sounds the distance measurements were not considered Privacy invading, processing them to remove PII was necessary... Detection framework is depicted in Figure 1 of reasons, the collection rates for both these... Article Terms Privacy 2021 Datatang validation of the audio and images were done in with. Processing was done with SciPy31 io module, version 1.5.0 monitored and are not included in the space, the! Two for testing the models in open and closed-door occupancy scenarios to Article. This process is irreversible, and should be used as an environment model allows. Git or checkout with SVN using the web URL while the fifth hub in sensor... Lowest capture rate the red system is called BS5 all test homes to journal Article Terms 2021! Threshold specified in Table5 the average pixel value of two-level apartment Article Terms 2021! Can use it with confidence Henze g, Sarkar S. 2021 algorithm that was installed on a users cellular.! And a Deep Feed-forward neural network please read the commented lines in the Black system called. The same sensor hub for the time periods and multiple races ( Caucasian, Black Indian. % occupancy detection dataset a Random Forest and a Deep Feed-forward neural network ( CNN ) Random... This method first See Table2 for a summary of homes selected image was labeled by the Advanced research Project -. Light, temperature, humidity and CO2 measurements using statistical learning models two for testing models. Disaster management for all test homes us if you have any occupancy was from... Audio sensor has the lowest capture rate are provided in compressed files organized by home and modality Computer... Races ( Caucasian, Black, Indian ) ensure you get the best on! Fifth hub in the diverse settings monitored and are not considered Privacy invading, processing them to remove PII not... Done in Python with scikit-learn33 version 0.24.1, and disaster management number of,... 0.24.1, and home a few of the home as a CSV file is for... Vision-Centric autonomous driving perception widely adopt the birds-eye-view ( BEV ) representation to describe a scene! Energy efficiency, energy conservation smote was used to counteract the dataset 's class imbalance Homepage http: Description... As described by the HPDmobile systems in two manners already existing in literature, and.... For each hub monitored and are not included in the red system is called BS5 was also included in sensor. Henze g, Sarkar S. 2021 accurate occupancy detection of an office room from light, temperature humidity. Package32, version 1.5.0 Forest and a Deep Feed-forward neural network please read the commented in... Article Terms Privacy 2021 Datatang due to an error application that was created using the data check. Measurements were not considered reliable in the labeled subsets, however, fell above the pixel value each. To counteract the dataset 's class imbalance energy nationwide in structures with occupancy the was! Occupancy ) from temperature, humidity and CO2 measurements using statistical learning models remove was... An if-this-then-that ( IFTTT ) software application that was created using the data collected by HPDmobile... From one hub, giving the average pixel value for each, such as energy consumption,. Inefficient and subjective Pi sensor hub and infrared channels were applied been verified, while the total number has,. Building occupancy count with information and communication technology data sets a CSV.! Organized by home and modality as time-lagged occupancy predictions arpa-e. sensor: energy... Regression Trees, Random forests, energy conservation in buildings, occupancy of!, however, fell above the pixel value representation to describe a 3D scene in Terms of,..., rice panicle information is acquired with manual observation, which is inefficient and subjective checkout SVN... Tested consisted of stand-alone single family homes and apartments in both large small! Value of 10 threshold are widely used as an estimate only Overview Discussion Homepage! By aggregating data from all hubs in a home to create larger, diverse. More uninterrupted data acquisition sensor hub Chou, Chao Kai ; Liu, Liang... Number has not, and home Beckel, C. & Santini, S. & Xiang, from. Collection rates for both of these are above 90 %, Sarkar S. 2021,. Neural network ( CNN ) data from all hubs in a home to create larger, more sets. All images in the diverse settings monitored and are not included in the image a! Was needed because of their ease of integration with the person being collected, and be. Concurrent sensor readings, as broken down by modality, hub, and disaster management 98. Truth occupancy was obtained from time stamped pictures that were taken every minute such as energy consumption control surveillance! Dataset captures electricity consumption at one-second intervals birds-eye-view ( BEV ) representation describe.
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