(d) Average pixel brightness: 10. 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. This method first Monthly energy review. 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). These predictions were compared to the collected ground truth data, and all false positive cases were identified. 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). Days refers to the number of days of data that were released from the home, while % Occ refers to the percentage of time the home was occupied by at least one person (for the days released). All data is collected with proper authorization with the person being collected, and customers can use it with confidence. Three data sets are submitted, for training and testing. Luis M. Candanedo, Vronique Feldheim. van Kemenade H, 2021. python-pillow/pillow: (8.3.1). Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. 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. In: ACS Sensors, Vol. To generate the different image sizes, the 112112 images were either downsized using bilinear interpolation, or up-sized by padding with a white border, to generate the desired image size. A review of building occupancy measurement systems. Since the data taking involved human subjects, approval from the federal Institutional Review Board (IRB) was obtained for all steps of the process. 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. Please read the commented lines in the model development file. In 2020, residential energy consumption accounted for 22% of the 98 PJ consumed through end-use sectors (primary energy use plus electricity purchased from the electric power sector) in the United States1, about 50% of which can be attributed to heating, ventilation, and air conditioning (HVAC) use2. U.S. Energy Information Administration. 0 datasets 89533 papers with code. Images that had an average value of less than 10 were deemed dark and not transferred off of the server. The 2022 perception and prediction challenges are now closed, but the leaderboards remain open for submissions. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. The SBCs are attached to a battery, which is plugged into the wall, and serves as an uninterruptible power supply to provide temporary power in the case of a brief power outage (they have a seven hour capacity). The final data that has been made public was chosen so as to maximize the amount of available data in continuous time-periods. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ), mobility sensors (i.e., passive infrared (PIR) sensors collecting mobility data) smart meters (i.e., energy consumption footprints) or cameras (i.e., visual ARPA-E. SENSOR: Saving energy nationwide in structures with occupancy recognition. 1a for a diagram of the hardware and network connections. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. The ECO dataset captures electricity consumption at one-second intervals. & Bernardino, A. sign in S.Y.T. It is understandable, however, why no datasets containing images and audio exist, as privacy concerns make capturing and publishing these data types difficult22. Hardware used in the data acquisition system. Python 2.7 is used during development and following libraries are required to run the code provided in the notebook: The Occupancy Detection dataset used, can be downloaded from the following link. Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. The optimal cut-off threshold that was used to classify an image as occupied or vacant was found through cross-validation and was unique for each hub. Full Paper Link: https://doi.org/10.1109/IC4ME253898.2021.9768582. Next, processing to validate the data and check for completeness was performed. Newsletter RC2022. If not considering the two hubs with missing modalities as described, the collection rates for both of these are above 90%. 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. If nothing happens, download Xcode and try again. Learn more. Gao, G. & Whitehouse, K. The self-programming thermostat: Optimizing setback schedules based on home occupancy patterns. 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. (b) Average pixel brightness: 43. Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. However, we are confident that the processing techniques applied to these modalities preserve the salient features of human presence. A High-Fidelity Residential Building Occupancy Detection Dataset Follow Posted on 2021-10-21 - 03:42 This repository contains data that was collected by the University of Colorado Boulder, with help from Iowa State University, for use in residential occupancy detection algorithm development. The data covers males and females (Chinese). WebDepending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. All images in the labeled subsets, however, fell above the pixel value of 10 threshold. The https:// ensures that you are connecting to the del Blanco CR, Carballeira P, Jaureguizar F, Garca N. Robust people indoor localization with omnidirectional cameras using a grid of spatial-aware classifiers. (e) H4: Main level of two-level apartment. Description Three data sets are submitted, for training and testing. Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. Despite the relative normalcy of the data collection periods, occupancy in the homes is rather high (ranging from 47% to 82% total time occupied). The scripts to reproduce exploratory figures. WebAbout Dataset Data Set Information: The experimental testbed for occupancy estimation was deployed in a 6m 4.6m room. government site. Weboccupancy-detection My attempt on the UCI Occupancy Detection dataset using various methods. Are you sure you want to create this branch? (d) Waveform after downsampling by integer factor of 100. 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. All were inexpensive and available to the public at the time of system development. 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. Opportunistic occupancy-count estimation using sensor fusion: A case study. Instead, they have been spot-checked and metrics for the accuracy of these labels are provided. Used Dataset link: https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. Seidel, R., Apitzsch, A. SMOTE was used to counteract the dataset's class imbalance. 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). 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. Wang F, et al. 2019. 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. You signed in with another tab or window. In addition to the environmental sensors mentioned, a distance sensor that uses time-of-flight technology was also included in the sensor hub. 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. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. U.S. Energy Information Administration. The age distribution ranges from teenager to senior. (b) Final sensor hub (attached to an external battery), as installed in the homes. 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. 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 Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. This outperforms most of the traditional machine learning models. 50 Types of Dynamic Gesture Recognition Data. 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