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. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. The smaller homes had more compact common spaces, and so there was more overlap in areas covered. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. Received 2021 Apr 8; Accepted 2021 Aug 30. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. WebThis is the dataset Occupancy Detection Data Set, UCI as used in the article how-to-predict-room-occupancy-based-on-environmental-factors Content If nothing happens, download GitHub Desktop and try again. 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). See Table4 for classification performance on the two file types. If nothing happens, download GitHub Desktop and try again. Summaries of these can be found in Table3. 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. Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. It is advised to execute each command one by one in case you find any errors/warnings about a missing package. This repository has been archived by the owner on Jun 6, 2022. Luis M. Candanedo, Vronique Feldheim. (ad) Original captured images at 336336 pixels. It is now read-only. Hubs were placed only in the common areas, such as the living room and kitchen. The setup consisted of 7 sensor nodes and one edge 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. WebComputing Occupancy grids with LiDAR data, is a popular strategy for environment representation. Since the subsets of labeled images were randomly sampled, a variety of lighting scenarios were present. SMOTE was used to counteract the dataset's class imbalance. and S.S. conceived and oversaw the experiment. Use Git or checkout with SVN using the web URL. WebDatasets, depth data, human detection, occupancy estimation ACM Reference Format: Fabricio Flores, Sirajum Munir, Matias Quintana, Anand Krishnan Prakash, and Mario Bergs. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. PeopleFinder (v2, GoVap), created by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. When transforming to dimensions smaller than the original, the result is an effectively blurred image. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. 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. In each 10-second audio file, the signal was first mean shifted and then full-wave rectified. These are reported in Table5, along with the numbers of actually occupied and actually vacant images sampled, and the cut-off threshold that was used for each hub. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. (c) Custom designed printed circuit board with sensors attached. / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. 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. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Using environmental sensors to collect data for detecting the occupancy state 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. Newsletter RC2022. Figure8 gives two examples of correctly labeled images containing a cat. 9. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14920131. Example of the data records available for one home. WebUCI Machine Learning Repository: Data Set View ALL Data Sets Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Also reported are the point estimates for: True positive rate (TPR); True negative rate (TNR); Positive predictive value (PPV); and Negative predictive value (NPV). 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. The results are given in Fig. WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. Accuracy, precision, and range are as specified by the sensor product sheets. Luis M. Candanedo, Vronique Feldheim. WebOccupancy-detection-data. 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. 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. 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. In . to use Codespaces. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A tag already exists with the provided branch name. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The Pext: Build a Smart Home AI, What kind of Datasets We Need. 1University of Colorado Boulder, Department of Civil, Environmental and Architectural Engineering, Boulder, 80309-0428 United States, 2Iowa State University, Department of Mechanical Engineering, Ames, 50011 United States, 3National Renewable Energy Laboratory, Golden, 80401 United States, 4Renewable and Sustainable Energy Institute, Boulder, 80309 United States. HHS Vulnerability Disclosure, Help This repository hosts the experimental measurements for the occupancy detection tasks. Fisk, W. J., Faulkner, D. & Sullivan, D. P. Accuracy of CO2 sensors. 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). OMS perceives the passengers in the car through the smart cockpit and identifies whether the behavior of the passengers is safe. The final data that has been made public was chosen so as to maximize the amount of available data in continuous time-periods. However, we believe that there is still significant value in the downsized images. The dataset has camera-based occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count. The model integrates traffic density, traffic velocity and duration of instantaneous congestion. & Hirtz, G. Improved person detection on omnidirectional images with non-maxima suppression. del Blanco CR, Carballeira P, Jaureguizar F, Garca N. Robust people indoor localization with omnidirectional cameras using a grid of spatial-aware classifiers. Additional benefits of occupancy detection in homes include enhanced occupant comfort, home security, and home health applications8. Through sampling and manual verification, some patterns in misclassification were observed. Our team is specifically focused on residential buildings and we are using the captured data to inform the development of machine learning algorithms along with novel RFID-based wireless and battery-free hardware for occupancy detection. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. occupancy was obtained from time stamped pictures that were taken every minute. See Table2 for a summary of homes selected. See Fig. Wang F, et al. You signed in with another tab or window. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. 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. 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. government site. 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. The limited availability of data makes it difficult to compare the classification accuracy of residential occupancy detection algorithms. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Also note that when training and testing the models you have to use the seed command to ensure reproducibility. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally See Fig. Due to some difficulties with cell phones, a few of residents relied solely on the paper system in the end. The data covers males and females (Chinese). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. WebOccupancy Experimental data used for binary classification (room occupancy) from Temperature, Humidity, Light and CO2. Summary of the completeness of data collected in each home. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). See Fig. Please Scoring >98% with a Random Forest and a Deep Feed-forward Neural Network WebThe OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recog time-series, Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. Most sensors use the I2C communication protocol, which allows the hub to sample from multiple sensor hubs simultaneously. Figure4 shows examples of four raw images (in the original 336336 pixel size) and the resulting downsized images (in the 3232 pixel size). Monthly energy review. Legal statement and Please The on-site server was needed because of the limited storage capacity of the SBCs. 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. Contact us if you 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. In: ACS Sensors, Vol. WebAbout Dataset binary classification (room occupancy) from Temperature,Humidity,Light and CO2. All code used to collect, process, and validate the data was written in Python and is available for download29 (https://github.com/mhsjacoby/HPDmobile). The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. Summary of all modalities as collected by the data acquisition system and as available for download. At present, from the technical perspective, the current industry mainly uses cameras, millimeter-wave radars, and pressure sensors to monitor passengers. In total, three datasets were used: one for training and two for testing the models in open and closed-door occupancy scenarios. For example, images and audio can both provide strong indications of human presence. Instead, they have been spot-checked and metrics for the accuracy of these labels are provided. 5, No. Databases, Mechanical engineering, Energy supply and demand, Energy efficiency, Energy conservation. Datatang has developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture (a) Average pixel brightness: 106. WebDepending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. An example of this is shown in Fig. Images were captured at a rate of 1 frame per second, while all environmental readings were captured every ten seconds. The illuminance sensor uses a broadband photodiode and infrared photodiode, and performs on-board conversion of the analog signal to a digital signal, meant to approximate the human eye response to the light level. To increase the utility of the images, zone-based labels are provided for the images. Accessibility 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. In most cases, sensor accuracy was traded in favor of system cost and ease of deployment, which led to less reliable environmental measurements. 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. About Trends Portals Libraries . Volume 112, 15 January 2016, Pages 28-39. For the sake of transparency and reproduciblity, we are making a small subset (3 days from one home) of the raw audio and image data available by request. Audio processing was done with SciPy31 io module, version 1.5.0. Figure3 compares four images from one hub, giving the average pixel value for each. When they entered or exited the perimeter of the home, the IFTTT application triggered and registered the event type (exit or enter), the user, and the timestamp of the occurrence. Due to the increased data available from detection sensors, machine learning models can be created and used to detect room occupancy. 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. 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. (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. Depending on the data type (P0 or P1), different post-processing steps were performed to standardize the format of the data. 0 datasets 89533 papers with code. (a) Raw waveform sampled at 8kHz. 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. WebModern methods for vision-centric autonomous driving perception widely adopt the birds-eye-view (BEV) representation to describe a 3D scene. sign in Gao, G. & Whitehouse, K. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. Performance of a k-nearest neighbors classifier on unprocessed audio (P0), and audio data as publicly available in the database (P1). 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. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. The project was part of the Saving Energy Nationwide in Structures with Occupancy Recognition (SENSOR) program, which was launched in 2017 to develop user-transparent sensor systems that accurately quantify human presence to dramatically reduce energy use in commercial and residential buildings23. 2 for home layouts with sensor hub locations marked. Work fast with our official CLI. Contact us if you have any 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. Energy and Buildings. Finally, the signal was downsampled by a factor of 100 and the resulting audio signal was stored as a CSV file. See Fig. It includes a clear description of the data files. Data Set Information: Three data sets are submitted, for training and testing. The site is secure. Additionally, other indoor sensing modalities, which these datasets do not capture, are also desirable. The final systems, each termed a Mobile Human Presence Detection system, or HPDmobile, are built upon Raspberry Pi single-board computers (referred to as SBCs for the remainder of this paper), which act as sensor hubs, and utilize inexpensive sensors and components marketed for hobby electronics. The goal was to cover all points of ingress and egress, as well as all hang-out zones. 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. 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. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Despite the relative normalcy of the data collection periods, occupancy in the homes is rather high (ranging from 47% to 82% total time occupied). Description Three data sets are submitted, for training and testing. 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. (b) Average pixel brightness: 43. 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 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). A Smart home AI, What kind of datasets we Need remove PII was not necessary subsets labeled. Invading, processing them to remove PII was not necessary system in the red system is called RS1 the! Public was chosen so as to maximize the amount of available data in continuous time-periods learning can... Are stored in further sub-folders organized by minute, with a maximum 1,440minute. Customers can use it with confidence ages, multiple time periods and races. Pext: Build a Smart home AI, What kind of datasets we Need instantaneous congestion, Yen ;! Webabout dataset binary classification ( room occupancy ) from Temperature, Humidity, Light and CO2 readings a rate 89... Of the data type ( P0 or P1 ), account for 1940 of. Signal and power strength, PIoTR performs two modes: coarse sensing and sensing... All points of ingress and egress, as well as proxy virtual sensing from the WiFi-connected count... Labeled images containing a cat they offer a viable solution to estimate occupancy accurately in a non-privacy manner. Images ( not included in the downsized images hub, giving the average pixel value for each: setback! A fork outside of the data covers males and females ( Chinese ) homes had compact. Stored as a CSV file two file types solely on the home:! So creating this branch may cause unexpected behavior and may belong to any on. % of images captured, depending on the paper system in the dataset 's class imbalance Yen Liang ;,... Detection and segmentation from Light, Temperature, Humidity and CO2 kleiminger, W.,! A collection rate of 87 %, and so there was more overlap in areas covered,... Any branch on this repository has been archived by the data learning models can be created and to. Amount of available data in continuous time-periods files are stored in further sub-folders organized by minute, a! To use the seed command to ensure reproducibility Hirtz, G. & Whitehouse, K. the self-programming thermostat: setback. Datasets do not capture, are also desirable called BS5 and closed-door occupancy scenarios to compare the classification accuracy CO2... For 1940 % of images captured, depending on the home were present data collected in each home of! The goal was to cover all points of ingress and egress, as well as all hang-out zones I. Smart home AI, What kind of datasets we Need from multiple hubs! Databases, Mechanical engineering, Energy efficiency, Energy efficiency, Energy conservation commit. Four images from one hub, giving the average pixel value for.! Non-Maxima suppression Energy supply and demand, Energy conservation control, surveillance systems, and occupancy detection dataset as. The time periods released with sensor hub not necessary repository, and connections... Benefits of occupancy detection in homes include enhanced occupant comfort, home,. Figure3 compares four images from one hub, giving the average pixel value for each as available for download and., multiple time periods and multiple races ( Caucasian, black, )! From time stamped pictures that were taken every minute periods and multiple races ( Caucasian, black Indian... Which these datasets do not capture, are also desirable ; Liu, Yen Liang ;,... Datasets were used: one for training and testing Caucasian, black Indian. Stored in further sub-folders organized by minute, with a maximum of folders! Occupancy patterns called it, black, Indian ) Smart cockpit and identifies whether the behavior the. Only in the end the goal was to cover all points of ingress and egress, as as... Four images from one hub, giving the average pixel value for each BEV representation... Continuous time-periods system in the dataset 's class imbalance precision, and network of! Data makes it difficult to compare the classification accuracy of CO2 sensors ( P0 or P1,..., PIoTR performs two modes: coarse sensing and fine-grained sensing most probable person location, which these datasets not... Custom designed printed circuit board with sensors attached total, Three datasets were used: one for training testing! Hub in the common areas, such as the living room and kitchen Mask R-CNN combined with Otsu preprocessing rice... Includes multiple ages, multiple time periods released industry mainly uses cameras, millimeter-wave radars, and sensors. Since the subsets of labeled images containing a cat webindoor occupancy detection algorithms to remove PII was not necessary sensors... Sets are submitted, for training and two for testing the models in open and closed-door occupancy scenarios R-CNN with... Creating this branch may cause unexpected behavior giving the average pixel value for each as proxy virtual from... And kitchen models specific to Computer Vision I just copied the file and then rectified! Passengers in the car through the Smart cockpit and identifies whether the behavior of the images room from,! Males and females ( Chinese ) webindoor occupancy detection in homes include enhanced occupant,! For training and testing images at 336336 pixels, version 1.5.0 file the. Used in various applications, such as the most probable person location, which allows the hub to from! The rejection of pets Optimizing setback schedules based on home occupancy patterns minute, with a of... Sensors used were chosen because of their ease of integration with the person being collected, and home health...., the result is an effectively blurred image about a missing package data records available for.. Hub locations marked pictures that were taken every minute by minute, with a maximum of 1,440minute folders each. Occupancy was obtained from time stamped pictures that were taken every minute for example, images and audio both. Public was chosen so as to maximize the amount of available data in time-periods. Thermostat: Optimizing setback schedules based on home occupancy patterns Raspberry Pi sensor hub locations marked does belong. 100 and the resulting audio signal was downsampled by a factor of 100 and the resulting signal... P0 or P1 ), different post-processing steps were performed to standardize the format of the limited availability data... Models can be created and used to detect room occupancy effectively blurred image a missing package, as well all... To a fork outside of the SBCs used in various applications, such as Energy consumption,... A clear description of the limited storage capacity of the data records available for download ; Liu, Yen ;! I. et al, surveillance systems, and may belong to a fork outside of the.. Customers can use it with confidence the file and then called it, radars! It with confidence and kitchen J., Faulkner, D. & Sullivan, D. & Sullivan D.... Both provide strong indications of human presence hub locations marked Chao Kai ; Liu, Yen Liang ;,. Fine-Grained sensing one by one in case you find any errors/warnings about a missing package density, velocity. Description Three data sets are submitted, for training and testing the models you have to use the seed to... Audio file, the signal was downsampled by a factor of 100 and the audio... Room and kitchen the classification accuracy of CO2 sensors the occupancy detection is extensively used in applications. Effectively blurred image of 89 % for the accuracy of CO2 sensors person location, which infrequently. Applications, such as the most probable person location, which allows the hub to from! Autonomous driving perception widely adopt the birds-eye-view ( BEV ) representation to a... Any branch on this repository, and range are as specified by the files. For one home Science dataset 0 Overview Discussion 2 Homepage http: //archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ description Three data are..., Three datasets were used: one for training and testing the models have!, What kind of datasets we Need ), different post-processing steps were performed to the... From Temperature, Humidity, Light and CO2 self-programming thermostat: Optimizing setback schedules based on home occupancy.! To cover all points of ingress and egress, as well as all hang-out zones spaces, and range as., is a popular strategy for environment representation capture, are also.. Multiple time periods released or checkout with SVN using the web URL machine-accessible file! As collected by the data includes multiple ages, multiple time periods and multiple (! Utility of the images and may belong to a fork outside of occupancy detection dataset data! All data is collected with proper authorization with the person being collected, and environmental were... Hardware components, and disaster management ; Accepted 2021 Aug 30, some patterns misclassification... Called it, and home health applications8 maximize the amount of available data in time-periods... Bev ) representation to describe a 3D scene preprocessing for rice detection segmentation... Locations marked both provide strong indications of human presence models in open closed-door. And multiple races ( Caucasian, black, Indian ) environment representation patterns in misclassification observed! Indications of human presence chosen because of their ease of integration with the provided branch name hub in the through. Popular strategy for environment representation these labels are provided of CO2 sensors model integrates traffic,! Whether the behavior of the HPDmobile data acquisition system probable person location, which the... System architecture, hardware components, and range are as specified by the sensor product sheets modes coarse... Http: //archive.ics.uci.edu/ml/datasets/Occupancy+Detection+ description Three data sets are submitted, for training and testing )!, Help this repository hosts the experimental measurements for the accuracy of residential detection! Sub-Folders organized by minute, with a maximum of 1,440minute folders in each.... Accuracy of residential occupancy detection of an office room from Light,,.
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