Evaluation of Commercial Truck Parking Detection for Rest Areas

2018 ◽  
Vol 2672 (9) ◽  
pp. 141-151
Author(s):  
Wei Sun ◽  
Ethan Stoop ◽  
Scott S. Washburn
Keyword(s):  
Software Tool ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Early Morning ◽  
Video Data ◽  
Sensor Data ◽  
Parking Space ◽  
Ground Truth Data ◽  
Detection Technology ◽  
Rest Areas

Florida’s interstate rest areas are heavily utilized by commercial trucks for overnight parking. Many of these rest areas regularly experience 100% utilization of available commercial truck parking spaces during the evening and early-morning hours. Being able to communicate availability of commercial truck parking space to drivers in advance of arriving at a rest area would reduce unnecessary stops at full rest areas as well as driver anxiety. In order to do this, it is critical to implement a vehicle detection technology to reflect the parking status of the rest area correctly. The objective of this project was to evaluate three different wireless in-pavement vehicle detection technologies as applied to commercial truck parking at interstate rest areas. This paper mainly focuses on the following aspects: (a) accuracy of the vehicle detection in parking spaces, (b) installation, setup, and maintenance of the vehicle detection technology, and (c) truck parking trends at the rest area study site. The final project report includes a more detailed summary of the evaluation. The research team recorded video of the rest areas as the ground-truth data and developed a software tool to compare the video data with the parking sensor data. Two accuracy tests (event accuracy and occupancy accuracy) were conducted to evaluate each sensor’s ability to reflect the status of each parking space correctly. Overall, it was found that all three technologies performed well, with accuracy rates of 95% or better for both tests. This result suggests that, for implementation, pricing, and/or maintenance issues may be more significant factors for the choice of technology.

scholarly journals 3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training

2020 ◽  
Vol 34 (07) ◽  
pp. 10631-10638
Author(s):  
Yu Cheng ◽  
Bo Yang ◽  
Bo Wang ◽  
Robby T. Tan
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
Video Data ◽  
Training Data ◽  
Human Pose Estimation ◽  
Ground Truth Data ◽  
Public Data ◽  
Spatio Temporal ◽  
Human Pose ◽  
3D Human Pose Estimation

Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in the recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, to our knowledge, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. As humans in videos may appear in different scales and have various motion speeds, we apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multi-stride temporal convolutional networks (TCNs) to estimate 3D joints or keypoints. Furthermore, we design a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement. During training, we explicitly mask out some keypoints to simulate various occlusion cases, from minor to severe occlusion, so that our network can learn better and becomes robust to various degrees of occlusion. As there are limited 3D ground truth data, we further utilize 2D video data to inject a semi-supervised learning capability to our network. Experiments on public data sets validate the effectiveness of our method, and our ablation studies show the strengths of our network's individual submodules.

On the potential and challenges of using machine-learning for automated quality control of environmental sensor data

2020 ◽  
Author(s):  
Lennart Schmidt ◽  
Hannes Mollenhauer ◽  
Corinna Rebmann ◽  
David Schäfer ◽  
Antje Claussnitzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Ground Truth ◽  
Sensor Data ◽  
Small Scale ◽  
Ground Truth Data ◽  
Starting Point ◽  
Environmental Sensor ◽  
Spatio Temporal ◽  
Automated Quality Control

<p>With more and more data being gathered from environmental sensor networks, the importance of automated quality-control (QC) routines to provide usable data in near-real time is becoming increasingly apparent. Machine-learning (ML) algorithms exhibit a high potential to this respect as they are able to exploit the spatio-temporal relation of multiple sensors to identify anomalies while allowing for non-linear functional relations in the data. In this study, we evaluate the potential of ML for automated QC on two spatio-temporal datasets at different spatial scales: One is a dataset of atmospheric variables at 53 stations across Northern Germany. The second dataset contains timeseries of soil moisture and temperature at 40 sensors at a small-scale measurement plot.</p><p>Furthermore, we investigate strategies to tackle three challenges that are commonly present when applying ML for QC: 1) As sensors might drop out, the ML models have to be designed to be robust against missing values in the input data. We address this by comparing different data imputation methods, coupled with a binary representation of whether a value is missing or not. 2) Quality flags that mark erroneous data points to serve as ground truth for model training might not be available. And 3) There is no guarantee that the system under study is stationary, which might render the outputs of a trained model useless in the future. To address 2) and 3), we frame the problem both as a supervised and unsupervised learning problem. Here, the use of unsupervised ML-models can be beneficial as they do not require ground truth data and can thus be retrained more easily should the system be subject to significant changes. In this presentation, we discuss the performance, advantages and drawbacks of the proposed strategies to tackle the aforementioned challenges. Thus, we provide a starting point for researchers in the largely untouched field of ML application for automated quality control of environmental sensor data.</p>

scholarly journals The Zurich urban micro aerial vehicle dataset

2017 ◽  
Vol 36 (3) ◽  
pp. 269-273 ◽  
Author(s):  
András L Majdik ◽  
Charles Till ◽  
Davide Scaramuzza
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Ground Level ◽  
Urban Environments ◽  
Sensor Data ◽  
Measurement Unit ◽  
Reconstruction Algorithms ◽  
Ground Truth Data ◽  
Micro Aerial Vehicle ◽  
Aerial Vehicle

This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5–15 m above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, global position system and inertial measurement unit sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.

scholarly journals Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 818-841
Author(s):  
Stanislav Timokhin ◽  
Mohammad Sadrani ◽  
Constantinos Antoniou
Keyword(s):  
Auxiliary Information ◽  
Ground Truth ◽  
Spatiotemporal Data ◽  
Sensor Data ◽  
Mobility Patterns ◽  
Ground Truth Data ◽  
Dependent Variables ◽  
Passive Sensor ◽  
Location Based Social Networks

Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.

scholarly journals The Study of Fresh-Water Lake Ice using Multiplexed Imaging Radar

1975 ◽  
Vol 15 (73) ◽  
pp. 461 ◽  
Author(s):  
M. Leonard Bryan ◽  
R. W. Larson
Keyword(s):  
Great Lakes ◽  
Ground Truth ◽  
Field Work ◽  
Sensor Data ◽  
Ice Thickness ◽  
Remotely Sensed Data ◽  
Radar Backscatter ◽  
Upper Great Lakes ◽  
Ground Truth Data ◽  
Adverse Weather

The study of ice in the upper Great Lakes, both from the operational and the scientific points of view, is receiving continued attention. Both quantitative and qualitative field work is being conducted to provide the needed background for accurate interpretation of remotely sensed data. The sensor data under discussion in this paper were obtained by a side-looking multiplexed airborne radar (SLAR). These were supplemented with ground-truth data. Radar, due to its ability to penetrate adverse weather, is an especially important instrument for monitoring ice in the upper Great Lakes. It has been previously shown that imaging radars can provide maps of ice cover in these areas. However, questions concerning both the nature of the surfaces reflecting radar energy and the interpretation of the radar imagery continually arise. Our analysis office in Whitefish Bay (Lake Superior) indicated that the combination of the ice/water interface with the ice/air interface is the major contributor to the radar backscatter as seen on the imagery. The ice has a very low dielectric constant (<3.0) and a low loss tangent. Thus, this ice is somewhat transparent to the energy used by the imaging SLAR system. The ice types studied include newly formed black ice, pancake ice, and frozen and consolidated pack and brash ice. Although ice thickness cannot be measured directly from the received signals, it is suspected that by combining the radar backscatter information with both meteorological and sea-state history of the area and with some basic ground truth, better estimates of the ice thickness may be provided. In addition, certain ice features (e.g. ridges, ice foots, areas of brash ice) may be identified with reasonable confidence. There is a continued need for additional ground work to verify the validity of imaging radars for these types of interpretations. This paper is being published in full in another issue of the Journal of Glaciology.

scholarly journals Land Surface Temperature Retrieval from LANDSAT-8 Ther-mal Infrared Sensor Data and Validation with Infrared Ther-mometer Camera

2018 ◽  
Vol 7 (4.20) ◽  
pp. 601
Author(s):  
Muhammad Mejbel Salih ◽  
Oday Zakariya Jasim ◽  
Khalid I. Hassoon ◽  
Aysar Jameel Abdalkadhum
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Ground Truth ◽  
Sensor Data ◽  
Landsat 8 ◽  
Infrared Sensor ◽  
Ground Truth Data ◽  
Land Cover Types

This paper illustrates a proposed method for the retrieval of land surface temperature (LST) from the two thermal bands of the LAND-SAT-8 data. LANDSAT-8, the latest satellite from Landsat series, launched on 11 February 2013, using LANDSAT-8 Operational Line Imager and Thermal Infrared Sensor (OLI & TIRS) satellite data. LANDSAT-8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12 bits. In this search a trial has been made to estimate LST over Al-Hashimiya district, south of Babylon province, middle of Iraq. Two dates images acquired on 2nd &18th of March 2018 to retrieve LST and compare them with ground truth data from infrared thermometer camera (all the meas-urements contacted with target by using type-k thermocouple) at the same time of images capture. The results showed that the rivers had a higher LST which is different to the other land cover types, of less than 3.47 C ◦, and the LST different for vegetation and residential area were less than 0.4 C ◦ with correlation coefficient of the two bands 10 and 11 Rbnad10= 0.70, Rband11 = 0.89 respectively, for the im-aged acquired on the 2nd of march 2018 and Rband10= 0.70 and Rband11 = 0.72 on the 18th of march 2018. These results confirm that the proposed approach is effective for the retrieval of LST from the LANDSAT-8 Thermal bands, and the IR thermometer camera data which is an effective way to validate and improve the performance of LST retrieval. Generally the results show that the closer measure-ment taken from the scene center time, a better quality to classify the land cover. The purpose of this study is to assess the use of LAND-SAT-8 data to specify temperature differences in land cover and compare the relationship between land surface temperature and land cover types.

scholarly journals IDENTIFICATION OF RICE CROP PHENOLOGY USING SCATSAT-1 KU-BAND SCATTEROMETER IN PUNJAB AND HARYANA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 549-555 ◽  
Author(s):  
P. Gaur ◽  
P. Tahlani ◽  
R. Tripathy ◽  
B. K. Bhattacharya ◽  
S. S. Ray
Keyword(s):  
Software Tool ◽  
Ground Truth ◽  
Development Stage ◽  
Rice Crop ◽  
Vegetative Stage ◽  
Backscatter Coefficient ◽  
Phenological Stages ◽  
Crop Phenology ◽  
Ground Truth Data ◽  
Ku Band

<p><strong>Abstract.</strong> The present study was carried out to analyse rice crop phenology using SCATSAT-1 Scatterometer data. Owing to its higher frequency, Ku-band responds to the dormant elements of surface, corresponding to top of crop canopy. The Ku-band backscatter behaviour is influenced by the structure, moisture content and the roughness of the top canopy which varies with different crop stages. Rice growth undergoes three main phenological stages viz, maximum tillering giving a scattered rough surface (first peak); maximum vegetative stage with a smooth top appearance (first minima after puddling); and the panicle development stage giving more scattering due to the spikelets (second peak). This study aimed at deriving these three stages of rice crop. Rice crop map generated, under FASAL project, using Sentinel-1 SAR data has been utilized and further the sub-pixel area within the SCATSAT-1 pixel. The daily Sigma-0 product was utilised for estimation of dates of major phenological stages of rice crop. A software tool was developed to generate the three phenological stages at 2&amp;thinsp;km pixel level using the time series backscatter coefficient in VV polarization over rice pixels using polynomial curve fitting. It was observed that peak tillering stage coincides with the first maxima, first minima coincides with the maximum vegetative stage and second maxima coincides with the peak heading or panicle formation stage of the crop. The validation was carried out using ground information from crop cutting experiments and ground truth data. Validation result showed 4 to 12 days difference in maximum vegetative stage.</p>

RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices (Preprint)

2020 ◽  
Author(s):  
Julio Vega ◽  
Meng Li ◽  
Kwesi Aguillera ◽  
Nikunj Goel ◽  
Echhit Joshi ◽  
...  
Keyword(s):  
Data Streams ◽  
Program Analysis ◽  
Data Science ◽  
Ground Truth ◽  
Mobile Sensing ◽  
Mobile Sensors ◽  
Sensor Data ◽  
Development Environment ◽  
Ground Truth Data ◽  
Reproducible Analysis

BACKGROUND Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms and devices. As a result, the quality of code varies, code is often not shared alongside publications, and when it is, it might not be stored on a version control system and most of the time there is no guarantee the development environment can be replicated. This makes it difficult for other scientists to read, reuse, audit, and reproduce a publication’s code and its results. OBJECTIVE We present RAPIDS, a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. METHODS RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by Snakemake and organized following the cookiecutter data science project. Its development has been and will be informed by public discussions with the mobile sensing research community. RESULTS We share open source, documented, extensible and tested code to preprocess and extract behavioral features from data collected with the AWARE Framework in Android and iOS smartphones as well as Fitbit devices. We also provide a file structure and development environment that other researchers can follow to publish their own models, visualizations, and reports. CONCLUSIONS RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and makes it easier to share an analysis workflow alongside publications.

scholarly journals The Study of Fresh-Water Lake Ice using Multiplexed Imaging Radar

1975 ◽  
Vol 15 (73) ◽  
pp. 461-461
Author(s):  
M. Leonard Bryan ◽  
R. W. Larson
Keyword(s):  
Great Lakes ◽  
Ground Truth ◽  
Field Work ◽  
Sensor Data ◽  
Ice Thickness ◽  
Remotely Sensed Data ◽  
Radar Backscatter ◽  
Upper Great Lakes ◽  
Ground Truth Data ◽  
Adverse Weather

The study of ice in the upper Great Lakes, both from the operational and the scientific points of view, is receiving continued attention. Both quantitative and qualitative field work is being conducted to provide the needed background for accurate interpretation of remotely sensed data. The sensor data under discussion in this paper were obtained by a side-looking multiplexed airborne radar (SLAR). These were supplemented with ground-truth data.Radar, due to its ability to penetrate adverse weather, is an especially important instrument for monitoring ice in the upper Great Lakes. It has been previously shown that imaging radars can provide maps of ice cover in these areas. However, questions concerning both the nature of the surfaces reflecting radar energy and the interpretation of the radar imagery continually arise.Our analysis office in Whitefish Bay (Lake Superior) indicated that the combination of the ice/water interface with the ice/air interface is the major contributor to the radar backscatter as seen on the imagery. The ice has a very low dielectric constant (<3.0) and a low loss tangent. Thus, this ice is somewhat transparent to the energy used by the imaging SLAR system. The ice types studied include newly formed black ice, pancake ice, and frozen and consolidated pack and brash ice.Although ice thickness cannot be measured directly from the received signals, it is suspected that by combining the radar backscatter information with both meteorological and sea-state history of the area and with some basic ground truth, better estimates of the ice thickness may be provided. In addition, certain ice features (e.g. ridges, ice foots, areas of brash ice) may be identified with reasonable confidence. There is a continued need for additional ground work to verify the validity of imaging radars for these types of interpretations.This paper is being published in full in another issue of the Journal of Glaciology.

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