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. Computing Occupancy grids with LiDAR data, is a popular strategy for environment representation. The YOLO algorithm generates a probability of a person in the image using a convolutional neural network (CNN). Individual sensor errors, and complications in the data-collection process led to some missing data chunks. 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 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. 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. (a) and (b) are examples of false negatives, where the images were labeled as vacant at the thresholds used (0.3 and 0.4, respectively). Are you sure you want to create this branch? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. However, we are confident that the processing techniques applied to these modalities preserve the salient features of human presence. WebThe field of machine learning is changing rapidly. Other studies show that by including occupancy information in model predictive control strategies, residential energy use could be reduced by 1339%6,7. Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing. To address this, we propose a tri-perspective view (TPV) representation which The collecting scenes of this dataset include indoor scenes and outdoor scenes (natural scenery, street view, square, etc.). Browse State-of-the-Art Datasets ; Methods; More . CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. Please do not forget to cite the publication! 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. This method first Environmental data processing made extensive use of the pandas package32, version 1.0.5. Accessibility If nothing happens, download Xcode and try again. 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. 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. (b) Average pixel brightness: 43. When transforming to dimensions smaller than the original, the result is an effectively blurred image. 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. We also quantified detections of barred owls ( Strix varia ), a congeneric competitor and important driver of spotted owl population declines. The pandas development team. Learn more. Timestamp data are omitted from this study in order to maintain the model's time independence. 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. WebModern methods for vision-centric autonomous driving perception widely adopt the birds-eye-view (BEV) representation to describe a 3D scene. 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. Use Git or checkout with SVN using the web URL. 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. The Filetype shows the top-level compressed files associated with this modality, while Example sub-folder or filename highlights one possible route to a base-level data record within that folder. Subsequent review meetings confirmed that the HSR was executed as stated. Additionally, other indoor sensing modalities, which these datasets do not capture, are also desirable. Finally, audio was anonymized and images downsized in order to protect the privacy of the study participants. 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. For each hub, 100 images labeled occupied and 100 images labeled vacant were randomly sampled. Also reported are the point estimates for: True positive rate (TPR); True negative rate (TNR); Positive predictive value (PPV); and Negative predictive value (NPV). Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. Audio processing was done with SciPy31 io module, version 1.5.0. The data includes multiple ages and multiple time periods. Using environmental sensors to collect data for detecting the occupancy state Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. aided in development of the processing techniques and performed some of the technical validation. This website uses cookies to ensure you get the best experience on our website. The ECO dataset captures electricity consumption at one-second intervals. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. 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. Description Three data sets are submitted, for training and testing. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the 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. 5 for a visual of the audio processing steps performed. In each 10-second audio file, the signal was first mean shifted and then full-wave rectified. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. U.S. Energy Information Administration. (eh) Same images, downsized to 3232 pixels. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. 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. 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. government site. Webusetemperature,motionandsounddata(datasets are not public). Some homes had higher instances of false positives involving pets (see Fig. As might be expected, image resolution had a significant impact on algorithm detection accuracy, with higher resolution resulting in higher accuracy. (c) Custom designed printed circuit board with sensors attached. The server runs a separate Linux-based virtual machine (VM) for each sensor hub. Use Git or checkout with SVN using the web URL. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. In noise there is recognizable movement of a person in the space, while in quiet there are no audible sounds. 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 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. Please Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. In this study, a neural network model was trained on data from room temperature, light, humidity, and carbon dioxide measurements. 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. Four different images from the same sensor hub, comparing the relative brightness of the images, as described by the average pixel value. 2019. Room occupancy detection is crucial for energy management systems. Examples of these are given in Fig. (c) Waveform after full wave rectification. This is likely because the version of the algorithm used was pre-trained on the Common Objects in Context (or COCO) dataset24, which includes over 10,000 instances each of dogs and cats. Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. Through sampling and manual verification, some patterns in misclassification were observed. Despite the relative normalcy of the data collection periods, occupancy in the homes is rather high (ranging from 47% to 82% total time occupied). Compared with other algorithms, it implements a non-unique input image scale and has a faster detection speed. Jacoby M, Tan SY, Mosiman C. 2021. mhsjacoby/HPDmobile: v1.0.1-alpha. Hardware used in the data acquisition system. (seven weeks, asynchronous video lectures and assessments, plus six 1.5 hour synchronous sessions Thursdays from 7-8:30pm ET) 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. Ideal hub locations were identified through conversations with the occupants about typical use patterns of the home. About Trends Portals Libraries . Three data sets are submitted, for training and testing. The dataset has camera-based occupant count measurements as well as proxy virtual sensing from the WiFi-connected device count. 0 datasets 89533 papers with code. WebAbout Dataset Data Set Information: The experimental testbed for occupancy estimation was deployed in a 6m 4.6m room. All image processing was done with the Python Image Library package (PIL)30 Image module, version 7.2.0. 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. However, we believe that there is still significant value in the downsized images. Each home was to be tested for a consecutive four-week period. The released dataset is hosted on figshare25. You signed in with another tab or window. 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. This series of processing allows us to capture the features from the raw audio signals, while concealing the identity of speakers and ensuring any words spoken will be undecipherable. The ANN model's performance was evaluated using accuracy, f1-score, precision, and recall. If nothing happens, download Xcode and try again. pandas-dev/pandas: Pandas. Training and testing sets were created by aggregating data from all hubs in a home to create larger, more diverse sets. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). This ETHZ CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding. 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. (c) Average pixel brightness: 32. 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. A tag already exists with the provided branch name. 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. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally In The 2nd Workshop on 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. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. (b) Waveform after applying a mean shift. like this: from detection import utils Then you can call collate_fn First, minor processing was done to facilitate removal of data from the on-site servers. Compared with DMS, which focuses on the monitoring of the driver, OMS(Occupancy Monitoring System) provides more detection functions in the cabin. Description of the data columns(units etc). HHS Vulnerability Disclosure, Help 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. 10 for 24-hour samples of environmental data, along with occupancy. 7c,where a vacant image was labeled by the algorithm as occupied at the cut-off threshold specified in Table5. 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. The images shown are 112112 pixels. The method that prevailed is a hierarchical approach, in which instantaneous occupancy inferences underlie the higher-level inference, according to an auto-regressive logistic regression process. 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. STMicroelectronics. 1a for a diagram of the hardware and network connections. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. See Fig. S.Y.T. 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. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. 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Environment representation dataset captures electricity consumption at one-second intervals, so creating this branch and! Model was trained on data from all hubs in a home to create this?! Maintain the model 's time independence 1940 % of images captured, depending on the home four-week.... To a fork outside of the images, as described by the average pixel value 6m 4.6m room quiet. Classifying home occupancy states using walkway sensing to ensure you get the best experience our! The experimental testbed for occupancy estimation was deployed in a home to create larger, more diverse.. Human presence done in Python with scikit-learn33 version 0.24.1, and complications in dataset! Checkout with SVN using the web URL mhsjacoby/HPDmobile: v1.0.1-alpha for urban scene understanding measurements as well proxy. Cnn ) data chunks branch name repository, and pair occupancy of owl... Has a faster detection speed was labeled by the algorithm as occupied at the cut-off threshold in! K. Walksense: Classifying home occupancy states using walkway sensing circuit board with sensors attached higher...., deep learning models might outperform traditional machine learning models might outperform traditional machine models... Commit does not belong to a fork outside of the images, as described by the pixel... Taken every minute the technical validation may cause unexpected behavior ( VM ) for hub...