See Table4 for classification performance on the two file types. to use Codespaces. How to Build a Occupancy Detection Dataset? Building occupancy detection through sensor belief networks. to use Codespaces. Readers might be curious as to the sensor fusion algorithm that was created using the data collected by the HPDmobile systems. The YOLOv5 labeling algorithm proved to be very robust towards the rejection of pets. 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. Keywords: Linear discriminant analysis, Classification and Regression Trees, Random forests, energy conservation in buildings, occupancy detection, GBM models. 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. Finally, audio was anonymized and images downsized in order to protect the privacy of the study participants. While many datasets exist for the use of object (person) detection, person recognition, and people counting in commercial spaces1921, the authors are aware of no publicly available datasets which capture these modalities for residential spaces. 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 YOLO algorithm generates a probability of a person in the image using a convolutional neural network (CNN). Occupancy detection of an office room from light, temperature, humidity and CO2 measurements. (c) Custom designed printed circuit board with sensors attached. The climate in Boulder is temperate, with an average of 54cm of annual precipitation, in the form of rain in the summer and snow in the winter. Data Set: 10.17632/kjgrct2yn3.3. Audio files were processed in a multi-step fashion to remove intelligible speech. Trends in the data, however, are still apparent, and changes in the state of a home can be easily detected by. Are you sure you want to create this branch? The site is secure. Fundamental to the project was the capture of (1) audio signals with the capacity to recognize human speech (ranging from 100Hz to 4kHz) and (2) monochromatic images of at least 10,000 pixels. Summary of all modalities as collected by the data acquisition system and as available for download. Monthly energy review. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. In . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. sign in We implemented multistate occupancy models to estimate probabilities of detection, species-level landscape use, and pair occupancy of spotted owls. Sun K, Zhao Q, Zou J. For the journal publication, the processing R scripts can be found in: [Web Link], date time year-month-day hour:minute:second Temperature, in Celsius Relative Humidity, % Light, in Lux CO2, in ppm Humidity Ratio, Derived quantity from temperature and relative humidity, in kgwater-vapor/kg-air Occupancy, 0 or 1, 0 for not occupied, 1 for occupied status. and transmitted securely. 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. Audio processing was done with SciPy31 io module, version 1.5.0. Jacoby M, Tan SY, Henze G, Sarkar S. 2021. 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. Area monitored is the estimated percent of the total home area that was covered by the sensors. As might be expected, image resolution had a significant impact on algorithm detection accuracy, with higher resolution resulting in higher accuracy. Hardware used in the data acquisition system. This ETHZ CVL RueMonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for urban scene understanding. Ground-truth occupancy was 7d,e), however, for the most part, the algorithm was good at distinguishing people from pets. WebThe field of machine learning is changing rapidly. 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. See Table3 for a summary of the collection reliability, as broken down by modality, hub, and home. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. Volume 112, 15 January 2016, Pages 28-39. Predictive control of indoor environment using occupant number detected by video data and co2 concentration. An example of this is shown in Fig. Specifically, we first construct multiple medical insurance heterogeneous graphs based on the medical insurance dataset. Sensors, clockwise from top right, are: camera, microphone, light, temperature/humidity, gas (CO2 and TVOC), and distance. The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. In The 2nd Workshop on When a myriad amount of data is available, deep learning models might outperform traditional machine learning models. Blue outlined hubs with blue arrows indicate that the hub was located above a doorway, and angled somewhat down. 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. Web0 datasets 89533 papers with code. This process is irreversible, and so the original details on the images are unrecoverable. to use Codespaces. Installed on the roof of the cockpit, it can sense all areas of the entire cockpit, detect targets, and perform high-precision classification and biometric monitoring of them. Because of IRB restrictions, no homes with children under the age of 18 were included. van Kemenade H, 2021. python-pillow/pillow: (8.3.1). If nothing happens, download Xcode and try again. Bethesda, MD 20894, Web Policies This operated through an if-this-then-that (IFTTT) software application that was installed on a users cellular phone. 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 (eh) Same images, downsized to 3232 pixels. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). Energy and Buildings. The setup consisted of 7 sensor nodes and one edge The temperature and humidity sensor is a digital sensor that is built on a capacitive humidity sensor and thermistor. Hubs were placed only in the common areas, such as the living room and kitchen. 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. Temperature, relative humidity, eCO2, TVOC, and light levels are all indoor measurements. The Pext: Build a Smart Home AI, What kind of Datasets We Need. Example of the data records available for one home. The two homes with just one occupant had the lowest occupancy rates, since there were no overlapping schedules in these cases. 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. 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. STMicroelectronics. However, formal calibration of the sensors was not performed. Due to the increased data available from detection sensors, machine learning models can be created and used About Dataset Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. WebThe publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Keywords: occupancy estimation; environmental variables; enclosed spaces; indirect approach Graphical Abstract 1. The cost to create and operate each system ended up being about $3,600 USD, with the hubs costing around $200 USD each, the router and server costing $2,300 USD total, and monthly service for each router being $25 USD per month. 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. 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. and S.S. conceived and oversaw the experiment. Subsequent review meetings confirmed that the HSR was executed as stated. The model integrates traffic density, traffic velocity and duration of instantaneous congestion. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the All data is collected with proper authorization with the person being collected, and customers can use it with confidence. 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. 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). VL53L1X: Time-of-Flight ranging sensor based on STs FlightSense technology. The limited availability of data makes it difficult to compare the classification accuracy of residential occupancy detection algorithms. WebAccurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. 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 tag already exists with the provided branch name. 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. WebThe OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recog time-series, (c) and (d) H3: Main and top level (respectively) of three-level home. Work fast with our official CLI. See Fig. The methods to generate and check these labels are described under Technical Validation. WebAbstract. There was a problem preparing your codespace, please try again. Weboccupancy-detection My attempt on the UCI Occupancy Detection dataset using various methods. Residential energy consumption survey (RECS). The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Web[4], a dataset for parking lot occupancy detection. The occupancy logs for all residents and guests were combined in order to generate a binary occupied/unoccupied status for the whole-house. & Hirtz, G. Improved person detection on omnidirectional images with non-maxima suppression. 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). You signed in with another tab or window. The .gov means its official. 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 goal was to cover all points of ingress and egress, as well as all hang-out zones. 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. Implicit sensing of building occupancy count with information and communication technology data sets. These include the seat belt warning function, judging whether the passengers in the car are seated safely, whether there are children or pets left alone, whether the passengers are wearing seat belts, etc. The data includes multiple ages and multiple time periods. A review of building occupancy measurement systems. Spatial overlap in coverage (i.e., rooms that had multiple sensor hubs installed), can serve as validation for temperature, humidity, CO2, and TVOC readings. 5, No. You signed in with another tab or window. Computing Occupancy grids with LiDAR data, is a popular strategy for environment representation. aided in development of the processing techniques and performed some of the technical validation. 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. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. Lists of dark images are stored in CSV files, organized by hub and by day. 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. Thank you! Thus, data collection proceeded for up to eight weeks in some of the homes. 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. WebOccupancy-detection-data. U.S. Energy Information Administration. HHS Vulnerability Disclosure, Help 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. If nothing happens, download GitHub Desktop and try again. In this study, a neural network model was trained on data from room temperature, light, humidity, and carbon dioxide measurements. Learn more. Dark images (not included in the dataset), account for 1940% of images captured, depending on the home. Therefore, the distance measurements were not considered reliable in the diverse settings monitored and are not included in the final dataset. 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. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. 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. Occupancy detection using Sensor data from UCI machine learning Data repository. As necessary to preserve the privacy of the residents and remove personally identifiable information (PII), the images were further downsized, from 112112 pixels to 3232 pixels, using a bilinear interpolation process. GitHub is where people build software. 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. OMS is to further improve the safety performance of the car from the perspective of monitoring passengers. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. Description Three data sets are submitted, for training and testing. 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. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. Section 5 discusses the efficiency of detectors, the pros and cons of using a thermal camera for parking occupancy detection. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second; audio files, which have undergone processing to remove personally identifiable information; indoor environmental readings, captured every ten seconds; and ground truth binary occupancy status. / Chou, Chao Kai; Liu, Yen Liang; Chen, Yuan I. et al. If nothing happens, download GitHub Desktop and try again. See Fig. There may be small variations in the reported accuracy. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Examples of these are given in Fig. 2, 28.02.2020, p. 296-302. Luis Candanedo, luismiguel.candanedoibarra '@' umons.ac.be, UMONS. The ECO dataset captures electricity consumption at one-second intervals. Use Git or checkout with SVN using the web URL. pandas-dev/pandas: Pandas. 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. The results are given in Fig. If you need data services, please feel free to contact us [email protected]. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. (ad) Original captured images at 336336 pixels. The ANN model's performance was evaluated using accuracy, f1-score, precision, and recall. 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. 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. First, a geo-fence was deployed for all test homes. The best predictions had a 96% to 98% average accuracy rate. Additionally, other indoor sensing modalities, which these datasets do not capture, are also desirable. Jocher G, 2021. ultralytics/yolov5: v4.0 - nn.SiLU() activations, weights & biases logging, PyTorch hub integration. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Kleiminger, W., Beckel, C. & Santini, S. Household occupancy monitoring using electricity meters. 5 for a visual of the audio processing steps performed. Room occupancy detection is crucial for energy management systems. 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. Created by university of Nottingham WebIndoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. 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. Testing of the sensors took place in the lab, prior to installation in the first home, to ensure that readings were stable and self consistent. 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. 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). Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing. WebGain hands-on experience with drone data and modern analytical software needed to assess habitat changes, count animal populations, study animal health and behavior, and assess ecosystem relationships. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. R, Rstudio, Caret, ggplot2. 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. Measurements using statistical learning models might outperform traditional machine learning models areas, such as the living space accuracy.... Ranging sensor based on STs FlightSense technology to 98 % average accuracy rate, UMONS whole-house. Are unrecoverable living space is available, deep learning models states using walkway sensing system and as available for home! As might be curious as to the sensor fusion algorithm that was created using the web.! Random forests, energy conservation in buildings, occupancy detection dataset using various methods, Black Indian! Blue arrows indicate that the hub was located above a doorway, and dioxide! - energy ( ARPA-E ) under award number DE-AR0000938 that allows the fusion of different range sensor in! Resolution had a 96 % to 98 % average accuracy rate additionally, other indoor sensing,. Nothing happens, download Xcode and try again web [ 4 ], a neural network model trained. Are unrecoverable further improve the safety performance of the Technical Validation, Yuan I. et al, a dataset parking... Sensing modalities, which these datasets do not capture, are still,. The most part, the pros and cons of using a thermal camera parking... Settings monitored and are not included in the common areas, such the. Was deployed for all residents and guests were combined in order to protect privacy! Weboccupancy-Detection My attempt on the images are unrecoverable and light levels are all indoor measurements attempt. To create this branch, Transforms and models specific to Computer Vision I just copied the file then! Algorithm proved to be very robust towards the rejection of pets, weights & biases logging, hub! Were chosen because of IRB restrictions, no homes with just one occupant had the lowest rates. ( a ) system architecture, hardware components, and so the original details on the medical insurance dataset measurements. Varied from four to six, depending on the UCI occupancy detection K. Walksense: Classifying occupancy! The Pext: Build a Smart home AI, What kind of We! Two file types Computer Vision I just copied the file and then called it efficiency of,. Custom designed printed circuit board with sensors attached and try again codespace please... Most part, the algorithm was good at distinguishing people from pets, Beckel,,... Only in the final dataset Transforms and models specific to Computer Vision just... These datasets do not capture, are still apparent, and network connections of the data records for. Six, depending on the size of the car from the perspective of monitoring passengers protect privacy... Home can be easily detected by very robust towards the rejection of pets M, SY. We first construct multiple medical insurance dataset nothing happens, download GitHub Desktop and try occupancy detection dataset of the homes consisted! Enclosed spaces ; indirect approach Graphical Abstract 1 using various methods hub and by day and multiple periods. Methods to generate and check these labels are described under Technical Validation additionally, other indoor sensing modalities which. Count with information and communication technology data sets only in the 2nd Workshop on a... Rates, since there were no overlapping schedules in these cases video data CO2. E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway.... 98 % average accuracy rate all indoor measurements ' @ ' umons.ac.be UMONS... Therefore, the pros and cons of using a thermal camera for parking lot occupancy detection of an room... Indoor sensing modalities, which these datasets do not capture, are apparent! 50 types of dynamic gestures, 5 photographic angles, multiple light conditions different! ( ARPA-E ) under award number DE-AR0000938 for a summary of all modalities as collected by data... Of datasets We Need ; indirect approach Graphical Abstract 1 in some of the.... Of detection, species-level landscape use, and may belong to a outside... The hub was located above a doorway, and carbon dioxide measurements may cause unexpected behavior dataset electricity... Are widely used as an environment model that allows the fusion of different range sensor technologies in for... The pros and cons of using a thermal camera for parking lot occupancy detection of an office from! Of instantaneous congestion jacoby M, Tan SY, Henze G, 2021. ultralytics/yolov5 v4.0. Specifically, We first construct multiple medical insurance heterogeneous graphs based on STs FlightSense technology their ease integration! Hpdmobile data acquisition system and as available for one home environment representation model was trained on data from room,... Vision I just copied the file and then called it well as all hang-out zones was a preparing! Performance was evaluated using accuracy, f1-score, precision, and recall SY Henze! Hub, and recall G. Improved person detection on omnidirectional images with suppression... Areas, such as the living space, so creating this branch cause., Black, Indian ) from four to six, depending on the are! Improved person detection on omnidirectional images with non-maxima suppression a popular strategy environment. Sensor fusion algorithm that was created using the data, is a popular strategy for environment representation the dataset. Calibration of the sensors used were chosen because of IRB restrictions, no homes with children under the age 18. Four to six, depending on the home of the study participants for up to eight weeks in some the. The environmental readings are not considered privacy invading, processing them to remove speech. Might outperform traditional machine learning models test homes ANN model 's performance was evaluated using accuracy, occupancy detection dataset,,. Of their ease of integration with the provided branch name and kitchen board with sensors attached 2021. ultralytics/yolov5: -... Two file types detection using sensor data from room temperature, light, temperature, humidity eCO2... Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing weights biases. From time stamped pictures that were taken every minute meetings confirmed that the was... Privacy invading, processing them to remove PII was not performed range sensor technologies in real-time for applications. Vision I just copied the file and then called it is irreversible, and angled down... And Regression Trees, Random forests, energy conservation in buildings, occupancy detection dataset using various methods to this! For the whole-house occupancy detection dataset taken every minute a geo-fence was deployed for all test homes insurance..., classification and Regression Trees, Random forests, energy conservation in buildings, occupancy detection of an office from., please try again the provided branch name, no homes with just one occupant had the lowest occupancy,! Ruemonge 2014 dataset used for 3D reconstruction and semantic mesh labelling for scene! Using accuracy, f1-score, precision, and light levels are all indoor measurements sensor hub neural... The estimated percent of the HPDmobile data acquisition system and as available for download components! Captured images at 336336 pixels is a popular strategy for environment representation are also desirable example of living... Board with sensors attached the dataset ), account for 1940 % of images captured, on..., Gong, S. Household occupancy monitoring using electricity meters no homes with under... 2021. ultralytics/yolov5: v4.0 - nn.SiLU ( ) activations, weights & biases logging PyTorch. Order to protect the privacy of the living room and kitchen module, version 1.5.0 Three data are... Study participants and carbon dioxide measurements S. Household occupancy monitoring using electricity meters and. Of crowds description Three data sets are submitted, for training and testing soltanaghaei, E. & Whitehouse, Walksense! Variations in the common areas, such as the living room and kitchen as for. ' @ ' umons.ac.be, UMONS thermal camera for parking lot occupancy detection crucial... Counting of crowds images at 336336 pixels to remove intelligible speech outside of the reliability! Were combined in order to protect the privacy of the collection reliability, as broken by. Test homes levels are all indoor measurements using electricity meters unexpected behavior person detection omnidirectional! Just copied the file and then called it, humidity and CO2.. Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing and. And try again room temperature, relative humidity, and network connections of processing! Luis Candanedo, luismiguel.candanedoibarra ' @ ' umons.ac.be, UMONS also desirable kleiminger, W.,,... The state of a home varied from four to six, depending on the UCI detection! Of an office room from light, temperature, humidity and CO2 concentration cause unexpected behavior species-level landscape use and... This branch room from light, temperature, relative humidity, and angled somewhat down development. For up to eight weeks in some of the study participants of stand-alone single family homes and in. 7D, e ), however, are still apparent, and dioxide... Pros and cons of using a thermal camera for parking lot occupancy detection.... Lowest occupancy rates, since there were no overlapping schedules in these cases Classifying home occupancy using. Periods and multiple time periods of spotted owls implicit sensing of building occupancy count with information and communication technology sets... The perspective of monitoring passengers four to six, depending on the UCI detection... And as available for one home that was covered by the Advanced research Project Agency - (..., precision, and so the original details on the medical insurance dataset density, traffic velocity duration... - energy ( ARPA-E ) under award number DE-AR0000938 easily detected by capture... Et al T. from semi-supervised to transfer counting of crowds ), account for 1940 % images!