scholarly journals Experimental Comparison between Event and Global Shutter Cameras

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1137
Author(s):  
Ondřej Holešovský ◽  
Radoslav Škoviera ◽  
Václav Hlaváč ◽  
Roman Vítek
Keyword(s):  
High Speed ◽  
Ground Truth ◽  
Motion Blur ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Experimental Comparison ◽  
Estimation Errors ◽  
Detection Rates ◽  
Ballistic Experiment ◽  
Event Camera

We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having a ground truth provided by an ultra-high-speed expensive frame-camera). The experimental results include sampling/detection rates and position estimation errors as functions of illuminance and motion speed; and the minimum pixel latency of two commercial state-of-the-art event-cameras (ATIS, DVS240). Event-cameras respond more slowly to positive than to negative large and sudden contrast changes. They outperformed a frame-camera in bandwidth efficiency in all our experiments. Both camera types provide comparable position estimation accuracy. The better event-camera was limited by pixel latency when tracking small objects, resulting in motion blur effects. Sensor bandwidth limited the event-camera in object recognition. However, future generations of event-cameras might alleviate bandwidth limitations.

scholarly journals Enhanced Localization with Adaptive Normal Distribution Transform Monte Carlo Localization for Map Based Navigation Robot

2019 ◽  
Vol 18 (3-2) ◽  
pp. 17-24 ◽  
Author(s):  
T.Y. Lim ◽  
C. F. Yeong ◽  
E. L. M. Su ◽  
S.M. Shithil ◽  
S.F. Chik ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Normal Distribution ◽  
High Speed ◽  
Research Work ◽  
Computational Effort ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Leibler Divergence ◽  
Monte Carlo Localization ◽  
Number Of Particles

Map-based navigation is the common navigation method used among the mobile robotic application. The localization plays an important role in the navigation where it estimates the robot position in an environment. Monte Carlo Localization (MCL) is found as the widely used estimation algorithm due to it non-linear characteristic. There are classifications of MCL such as Adaptive MCL (AMCL), Normal Distribution Transform MCL (NDT-MCL) which can perform better than the MCL. However, AMCL is adaptive to particles but the position estimation accuracy is not optimized. NDT-MCL has good position estimation but it requires higher number of particles which results in higher computational effort. The objective of the research is to design and develop a localization algorithm which can achieve better performance in term of position estimation and computational effort. The new MCL algorithm which is named as Adaptive Normal Distribution Transform Monte Carlo Localization (ANDT-MCL) is then designed and developed. It integrates Kullback–Leibler divergence, Normal Distribution Transform and Systematic Resampling into the algorithm. Three experiments are conducted to evaluate the performance of proposed ANDT-MCL in simulated environment. These experiments include evaluating the performance of ANDT-MCL with different path shape, distance and velocity. In the end of the research work, the proposed ANDT-MCL is successfully developed. It is adaptive to the number of particles used, higher position estimation and lower computational effort than existing algorithms. The algorithm can produce better position estimation with less computational effort in any kind paths and is consistent in long journey as well as can outperform in high speed navigation.

Analysis of Stator Resistance Effects in Carrier Signal Injection Based Sensorless Control of Permanent Magnet Synchronous Machine

2011 ◽  
Vol 383-390 ◽  
pp. 5951-5957 ◽  
Author(s):  
Jian Min Wang ◽  
Shi Xia Tian
Keyword(s):  
Sensorless Control ◽  
Estimation Error ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Estimation Errors ◽  
Injection Method ◽  
Signal Injection ◽  
Carrier Signal Injection ◽  
Rotor Position Estimation ◽  
Stator Resistance

This paper analyzes the effects of stator resistance on rotor position estimation accuracy in carrier signal injection based sensorless control of PMSM. Carrier current expressions are derived for both rotating and pulsating voltage injection method when the stator resistance is taken into account. Position estimation errors resulted from stator resistance are analyzed theoretically and investigated by simulation. It is shown that the influences of stator resistance on above two injection methods are quite different. The stator resistance will result in a position estimation error in the rotating voltage injection method. But it does not affect the position estimation accuracy in the pulsating voltage injection method as long as a suitable signal extracting method is used.

scholarly journals The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM

2017 ◽  
Vol 36 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Elias Mueggler ◽  
Henri Rebecq ◽  
Guillermo Gallego ◽  
Tobi Delbruck ◽  
Davide Scaramuzza
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Dynamic Range ◽  
Ground Truth ◽  
High Dynamic Range ◽  
High Temporal Resolution ◽  
Event Camera ◽  
High Dynamic ◽  
Event Based ◽  
New Algorithms

New vision sensors, such as the dynamic and active-pixel vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e. rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.

Experimental research of indoor navigation system based on broadcast signals

2020 ◽  
Vol 49 (5) ◽  
pp. 49-57
Author(s):  
A. V. Ksendzuk ◽  
E. A. Surmin ◽  
V. V. Kachesov ◽  
S. O. Zhdanov ◽  
K. S. Shakhalov
Keyword(s):  
Navigation System ◽  
Open Space ◽  
Position Estimation ◽  
Indoor Navigation ◽  
Estimation Accuracy ◽  
Satellite Systems ◽  
Indoor Navigation System ◽  
Point To Point ◽  
Navigation Signals ◽  
Navigation Equipment

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

scholarly journals RCVC: RSU-Aided Cluster-Based Vehicular Clouds Architecture for Urban Areas

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 193
Author(s):  
Mohamed Ben bezziane ◽  
Ahmed Korichi ◽  
Chaker Abdelaziz Kerrache ◽  
Mohamed el Amine Fekair
Keyword(s):  
Urban Areas ◽  
High Speed ◽  
Service Discovery ◽  
Cluster Head ◽  
Experimental Comparison ◽  
The Road ◽  
Road Side Unit ◽  
End To End Delay ◽  
End To End ◽  
On The Road

As a promising topic of research, Vehicular Cloud (VC) incorporates cloud computing and ad-hoc vehicular network (VANET). In VC, supplier vehicles provide their services to consumer vehicles in real-time. These services have a significant impact on the applications of internet access, storage and data. Due to the high-speed mobility of vehicles, users in consumer vehicles need a mechanism to discover services in their vicinity. Besides this, quality of service varies from one supplier vehicle to another; thus, consumer vehicles attempt to pick out the most appropriate services. In this paper, we propose a novel protocol named RSU-aided Cluster-based Vehicular Clouds protocol (RCVC), which constructs the VC using the Road Side Unit (RSU) directory and Cluster Head (CH) directory to make the resources of supplier vehicles more visible. While clusters of vehicles that move on the same road form a mobile cloud, the remaining vehicles form a different cloud on the road side unit. Furthermore, the consumption operation is achieved via the service selection method, which is managed by the CHs and RSUs based on a mathematical model to select the best services. Simulation results prove the effectiveness of our protocol in terms of service discovery and end-to-end delay, where we achieved service discovery and end-to-end delay of 3 × 10−3 s and 13 × 10−2 s, respectively. Moreover, we carried out an experimental comparison, revealing that the proposed method outperformed several states of the art protocols.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Sensorless Pedalling Torque Estimation Based on Motor Load Torque Observation for Electrically Assisted Bicycles

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 88
Author(s):  
Riccardo Mandriota ◽  
Stefano Fabbri ◽  
Matthias Nienhaus ◽  
Emanuele Grasso
Keyword(s):  
External Disturbance ◽  
State Observer ◽  
Position Estimation ◽  
Load Torque ◽  
Estimation Errors ◽  
Position Information ◽  
Longitudinal Dynamics ◽  
Rotor Position ◽  
Electrically Assisted ◽  
Motor Load

The need for reducing the cost of and space in Electrically Assisted Bicycles (EABs) has led the research to the development of solutions able to sense the applied pedalling torque and to provide a suitable electrical assistance avoiding the installation of torque sensors. Among these approaches, this paper proposes a novel method for the estimation of the pedalling torque starting from an estimation of the motor load torque given by a Load Torque Observer (LTO) and evaluating the environmental disturbances that act on the vehicle longitudinal dynamics. Moreover, this work shows the robustness of this approach to rotor position estimation errors introduced when sensorless techniques are used to control the motor. Therefore, this method allows removing also position sensors leading to an additional cost and space reduction. After a mathematical description of the vehicle longitudinal dynamics, this work proposes a state observer capable of estimating the applied pedalling torque. The theory is validated by means of experimental results performed on a bicycle under different conditions and exploiting the Direct Flux Control (DFC) sensorless technique to obtain the rotor position information. Afterwards, the identification of the system parameters together with the tuning of the control system and of the LTO required for the validation of the proposed theory are thoroughly described. Finally, the capabilities of the state observer of estimating an applied pedalling torque and of recognizing the application of external disturbance torques to the motor is verified.

scholarly journals Tracking by Deblatting

2021 ◽  
Author(s):  
Denys Rozumnyi ◽  
Jan Kotera ◽  
Filip Šroubek ◽  
Jiří Matas
Keyword(s):  
High Speed ◽  
High Performance ◽  
Motion Blur ◽  
Velocity Estimation ◽  
High Speed Camera ◽  
Trajectory Function ◽  
Novel Approach ◽  
Blind Deblurring ◽  
Object Trajectories ◽  
Complex Trajectories

AbstractObjects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects travel a considerable distance during exposure time of a single frame, and therefore, their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by general trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. By postprocessing, non-causal Tracking by Deblatting estimates continuous, complete, and accurate object trajectories for the whole sequence. Tracked objects are precisely localized with higher temporal resolution than by conventional trackers. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are then fitted to smooth trajectory segments between bounces. The output is a continuous trajectory function that assigns location for every real-valued time stamp from zero to the number of frames. The proposed algorithm was evaluated on a newly created dataset of videos from a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms the baselines both in recall and trajectory accuracy. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity, and sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall, and velocity estimation.

syris: a flexible and efficient framework for X-ray imaging experiments simulation

2017 ◽  
Vol 24 (6) ◽  
pp. 1283-1295 ◽  
Author(s):  
Tomáš Faragó ◽  
Petr Mikulík ◽  
Alexey Ershov ◽  
Matthias Vogelgesang ◽  
Daniel Hänschke ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Data Processing ◽  
Graphics Processing Units ◽  
High Speed ◽  
Real Data ◽  
Estimation Accuracy ◽  
Processing Parameter ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Conditions

An open-source framework for conducting a broad range of virtual X-ray imaging experiments,syris, is presented. The simulated wavefield created by a source propagates through an arbitrary number of objects until it reaches a detector. The objects in the light path and the source are time-dependent, which enables simulations of dynamic experiments,e.g.four-dimensional time-resolved tomography and laminography. The high-level interface ofsyrisis written in Python and its modularity makes the framework very flexible. The computationally demanding parts behind this interface are implemented in OpenCL, which enables fast calculations on modern graphics processing units. The combination of flexibility and speed opens new possibilities for studying novel imaging methods and systematic search of optimal combinations of measurement conditions and data processing parameters. This can help to increase the success rates and efficiency of valuable synchrotron beam time. To demonstrate the capabilities of the framework, various experiments have been simulated and compared with real data. To show the use case of measurement and data processing parameter optimization based on simulation, a virtual counterpart of a high-speed radiography experiment was created and the simulated data were used to select a suitable motion estimation algorithm; one of its parameters was optimized in order to achieve the best motion estimation accuracy when applied on the real data.syriswas also used to simulate tomographic data sets under various imaging conditions which impact the tomographic reconstruction accuracy, and it is shown how the accuracy may guide the selection of imaging conditions for particular use cases.

scholarly journals Enhancement of data transmission using optimized multi-cell approach in 5G backhaul wireless mesh network

2019 ◽  
Vol 14 (1) ◽  
pp. 65
Author(s):  
Nirmalkumar S Benni ◽  
Sunilkumar S Manvi
Keyword(s):  
Data Transmission ◽  
High Speed ◽  
User Equipment ◽  
Mesh Network ◽  
Wave Band ◽  
Estimation Errors ◽  
Traffic Routing ◽  
Centimeter Wave ◽  
Channel Information ◽  
High Speed Data

<p><span>The millimeter wave (mmWave) band and its usage has taken an attention in this 5G creation as its implementation can provide ultra-high speed data transmission in wireless network. This is much better than the centimeter wave-band as it has limitations on the bandwidth usage. While considering the 5G networking with the backhaul-topologies, the network and user-equipment performance depends on the selection of serving receiving nodes (RNs) of the user-equipment (UE). Therefore, the algorithms that reduces the complexity of the UE association and the backhaul traffic-routing must be chosen, which thereby maximizes the efficiency of the spectrum and energy of network. The modifications are done to the Multicellular local solution (McLS)approach, which leads to our proposed algorithm that is, improved against the channel information estimation-errors. The optimized multi-cell approach (OMcA) is formed by combining the improved-parameter, the pre-coder, mean square error (MSE) weighted-variable and receive filter. The proposed OMcA is compared with some other existing technique in order to evaluate the performance in terms of sum rate (SR).</span></p>

Sign in / Sign up

Export Citation Format

Share Document