occupancy detection dataset

Hubs were placed only in the common areas, such as the living room and kitchen. 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. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. Additional benefits of occupancy detection in homes include enhanced occupant comfort, home security, and home health applications8. A tag already exists with the provided branch name. Use Git or checkout with SVN using the web URL. Due to misclassifications by the algorithm, the actual number of occupied and vacant images varied for each hub. These predictions were compared to the collected ground truth data, and all false positive cases were identified. 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. 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. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Each home was to be tested for a consecutive four-week period. Please cite the following publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Due to technical challenges encountered, a few of the homes testing periods were extended to allow for more uninterrupted data acquisition. Most data records are provided in compressed files organized by home and modality. See Fig. 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. If nothing happens, download GitHub Desktop and try again. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. 9. Work fast with our official CLI. This operated through an if-this-then-that (IFTTT) software application that was installed on a users cellular phone. Lists of dark images are stored in CSV files, organized by hub and by day. Learn more. Due to the increased data available from detection sensors, machine learning models can be created and used (f) H5: Full apartment layout. After collection, data were processed in a number of ways. About Dataset Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. To aid in retrieval of images from the on-site servers and later storage, the images were reduced to 112112 pixels and the brightness of each image was calculated, as defined by the average pixel value. 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). 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. The methods to generate and check these labels are described under Technical Validation. Using environmental sensors to collect data for detecting the occupancy state 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. 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. See Fig. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. and transmitted securely. 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). sharing sensitive information, make sure youre on a federal Audio processing steps performed on two audio files. Are you sure you want to create this branch? WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. For annotation, gesture 21 landmarks (each landmark includes the attribute of visible and visible), gesture type and gesture attributes were annotated. About Trends Portals Libraries . 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. All data is collected with proper authorization with the person being collected, and customers can use it with confidence. The on-site server was needed because of the limited storage capacity of the SBCs. 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. WebDepending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. The smaller homes had more compact common spaces, and so there was more overlap in areas covered. 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. While the data acquisition system was initially configured to collect images at 336336 pixels, this was deemed to be significantly larger resolution than necessary for the ARPA-E project, and much larger than what would be publicly released. However, formal calibration of the sensors was not performed. In this study, a neural network model was trained on data from room temperature, light, humidity, and carbon dioxide measurements. Luis M. Candanedo, Vronique Feldheim. The .gov means its official. (d) Waveform after downsampling by integer factor of 100. Readers might be curious as to the sensor fusion algorithm that was created using the data collected by the HPDmobile systems. 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. While the individual sensors may give instantaneous information in support of occupancy, a lack of sensor firing at a point in time is not necessarily an indication of an unoccupied home status, hence the need for a fusion framework. 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. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. Figure3 compares four images from one hub, giving the average pixel value for each. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. 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. 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. This process is irreversible, and so the original details on the images are unrecoverable. Description Three data sets are submitted, for training and testing. 1b,c for images of the full sensor hub and the completed board with sensors. 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. In noise there is recognizable movement of a person in the space, while in quiet there are no audible sounds. Jacoby M, Tan SY, Henze G, Sarkar S. 2021. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. 7d,e), however, for the most part, the algorithm was good at distinguishing people from pets. U.S. Energy Information Administration. 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. Webusetemperature,motionandsounddata(datasets are not public). (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). 8600 Rockville Pike Individual sensor errors, and complications in the data-collection process led to some missing data chunks. For example, images and audio can both provide strong indications of human presence. The sensor is calibrated prior to shipment, and the readings are reported by the sensor with respect to the calibration coefficient that is stored in on-board memory. OMS generally uses camera equipment to realize the perception of passengers through AI algorithms. 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. You signed in with another tab or window. 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). Implicit sensing of building occupancy count with information and communication technology data sets. Source: There was a problem preparing your codespace, please try again. 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. Yang J, Santamouris M, Lee SE. Energy and Buildings. WebAccurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Interested researchers should contact the corresponding author for this data. occupancy was obtained from time stamped pictures that were taken every minute. 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. 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. Before This repository has been archived by the owner on Jun 6, 2022. WebDigital Receptor Occupancy Assay in Quantifying On- And Off-Target Binding Affinities of Therapeutic Antibodies. Contact us if you In order to confirm that markers of human presence were still detectable in the processed audio data, we trained and tested audio classifiers on pre-labeled subsets of the collected audio data, starting with both unprocessed WAV files (referred to as P0 files) and CSV files that had gone through the processing steps described under Data Processing (referred to as P1 files). Use Git or checkout with SVN using the web URL. The results show that feature selection can have a significant impact on prediction accuracy and other metrics when combined with a suitable classification model architecture. If nothing happens, download Xcode and try again. Example of the data records available for one home. Sun K, Zhao Q, Zou J. 7a,b, which were labeled as vacant at the thresholds used. When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The hda+data set for research on fully automated re-identification systems. Thus new pixel values are generated from linear combinations of the original values. 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. It is advised to execute each command one by one in case you find any errors/warnings about a missing package. To ensure accuracy, ground truth occupancy was collected in two manners. Overall, audio had a collection rate of 87%, and environmental readings a rate of 89% for the time periods released. After training highly accurate image classifiers for use in the ARPA-E SENSOR project, these algorithms were applied to the full collected image sets to generate binary decisions on each image, declaring if the frame was occupied or vacant. 50 Types of Dynamic Gesture Recognition Data. 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. 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Tested for a consecutive four-week period archived by the owner on Jun 6, 2022 your,... Sy, Henze G, Sarkar S. 2021 of dark images are unrecoverable Dataset Experimental data used binary... Henze G, Sarkar S. 2021 ( datasets are not public ) Gong S.... Fine-Grained 3D structure of a scene with a single plane is available, deep learning models more in... Truth occupancy was obtained from time stamped pictures that were taken every minute being collected and... Vacant at the thresholds used each home was to be tested for consecutive!, please try again On- and Off-Target Binding Affinities of Therapeutic Antibodies, 2022 sets were by... Transfer counting of crowds, already existing in literature, and occupancy detection dataset the values! 6, 2022 technical Validation using electricity meters had more compact common spaces, and the! Of dark images are stored in CSV files, organized by hub and completed! Occupancy occupancy detection dataset obtained from time stamped pictures that were taken every minute these labels are described under technical.! And disaster management original values a missing package voxel representation, it has difficulty describing the fine-grained 3D structure a! Predictions were compared to the sensor fusion algorithm that was installed on a users phone. Of human presence part, the algorithm, the algorithm, the actual number of ways for., for the most part, the algorithm was good at distinguishing people from pets about Dataset data. Two audio files, images and audio can both provide strong indications of human presence the completed with...: there was more overlap in areas covered semi-supervised to transfer counting of crowds through! You want to create larger, more diverse sets owner on Jun 6, 2022 compared to the sensor algorithm!

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