scholarly journals A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 774
Author(s):  
Heng Zhang ◽  
Hongwu Zhan ◽  
Libin Zhang ◽  
Fang Xu ◽  
Xinbin Ding
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
High Speed ◽  
Correlation Filter ◽  
Moving Targets ◽  
Vision Measurement ◽  
Time Operation ◽  
Micro Robot ◽  
Real Time Operation ◽  
Inter Frame

This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system.

scholarly journals Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Environmental Changes ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Target Model ◽  
Change Of Scale ◽  
Tracking Model ◽  
Spatio Temporal

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

Performance Analysis of Adaptive Control Technique for Aircraft Gas Turbine Engine

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
S. Ganesan ◽  
R. Jaganraj ◽  
G.M. Priyadharshini
Keyword(s):  
Adaptive Control ◽  
High Speed ◽  
Aircraft Engine ◽  
Control Technique ◽  
Atmospheric Conditions ◽  
Turbine Engine ◽  
Time Operation ◽  
Real Time Operation ◽  
Speed Mode ◽  
Discharge Pressure

This paper presents an adaptive control technique to compensate the thrust variation in an aircraft engine whose performance has been disturbed due to atmospheric conditions. The course of dysfunction appears when a large throttle transient is performed such that the engine switched from low to high speed mode. A relationship is observed between engine disturbance and the overshoot in engine shaft rpm or compressor discharge pressure or turbine temperature, which is determined to cause the thrust variation. This relationship is used to adapt a control. This method works very well up to the operability limit of an engine. Additionally, the type of disturbance identified from sensors data will be useful to implement the adaptive control in real time operation.

Rotating Shape Modeling with SPIDAR

1992 ◽  
Vol 4 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Makoto Sato ◽  
◽  
Shun-ichi Numazaki ◽  
Yukihiro Hirata ◽  
Hiroshi Kawarada
Keyword(s):  
High Speed ◽  
Real Space ◽  
Shape Modeling ◽  
Deformation Model ◽  
3 Dimensional ◽  
Human Interface ◽  
3D Objects ◽  
Time Operation ◽  
Real Time Operation ◽  
Object Models

To develop a human interface for shape modeling of 3-dimensional (3D) objects, it is necessary to construct a “virtual workspace” where we can directly manipulate object models as in real space. We have proposed Space Interface Device for Artificial Reality (SPIDAR) for a virtual workspace. Using SPIDAR, we can operate in a virtual workspace with visual and force sensation. We have developed a rotating shape modeling system that simulates a turntable similar to that used for modeling clay. For real-time operation, we have considered object volume and have proposed an algorithm of high-speed and natural deformation model under the condition of constant volume.

scholarly journals Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1084 ◽  
Author(s):  
Dong-Hyun Lee
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Multiple Scale ◽  
Visual Object ◽  
Feature Maps ◽  
Visual Object Tracking ◽  
Performance Improvements ◽  
Time Operation ◽  
Real Time Operation ◽  
Speed Performance

The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC.

scholarly journals High-accuracy localization for indoor group users based on extended Kalman filter

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881272 ◽  
Author(s):  
Tian Wang ◽  
Yuzhu Liang ◽  
Yaxin Mei ◽  
Muhammad Arif ◽  
Chunsheng Zhu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Localization ◽  
High Accuracy ◽  
Mutual Distance ◽  
Distance Information ◽  
Localization Accuracy ◽  
Accuracy Requirement ◽  
Time Operation ◽  
Real Time Operation

Indoor localization has attracted increasing research attentions in the recent years. However, many important issues still need to be further studied to keep pace with new requirements and technical progress, such as real-time operation, high accuracy, and energy efficiency. In order to meet the high localization accuracy requirement and the high localization dependable requirement in some scenarios, we take the users as a group to utilize the mutual distance information among them to get better localization performance. Moreover, we design a mobile group localization method based on extended kalman filter and believable factor of non-localized nodes, which can alleviate the influence caused by environmental noisy and unstable wireless signals to improve the localization accuracy. Besides, we implement a real system based on ZigBee technique and perform experiments on the campus of Huaqiao University. Experimental results and theoretical analysis validate the effectiveness of the proposed method.

scholarly journals Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1129 ◽  
Author(s):  
Jianming Zhang ◽  
Yang Liu ◽  
Hehua Liu ◽  
Jin Wang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Correlation Filter ◽  
Appearance Model ◽  
Visual Object ◽  
Multiple Correlation ◽  
Correlation Filters ◽  
Tracking Algorithms ◽  
Global Correlation ◽  
Object Appearance

Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local–global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local–global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.

scholarly journals “Reading Pictures Instead of Looking”: RGB-D Image-Based Action Recognition via Capsule Network and Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2217
Author(s):  
Botong Zhao ◽  
Yanjie Wang ◽  
Keke Su ◽  
Hong Ren ◽  
Haichao Sun
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Action Recognition ◽  
Recognition Algorithm ◽  
Dendritic Network ◽  
Time Operation ◽  
The Neural Network ◽  
Real Time Operation ◽  
Spatial Coordinates ◽  
Depth Learning

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called “Reading Pictures Instead of Looking” (RPIL). This method resolves the convolutional neural network’s over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body. The detected parts and their attribute parameters (e.g., spatial coordinates, color) are then analyzed by Bert. A Kalman filter analyzes the predicted capsules and filters out any misinformation to prevent the action recognition results from being affected by incorrectly predicted capsules. The parameters between neuron layers are evaluated, then the structure is pruned into a dendritic network to enhance the computational efficiency of the algorithm. This minimizes the dependence of in-depth learning on the random features extracted by the CNN without sacrificing the model’s accuracy. The association between hidden layers of the neural network is also explained. With a 90% observation rate, the OAD dataset test precision is 83.3%, the ChaLearn Gesture dataset test precision is 72.2%, and the G3D dataset test precision is 86.5%. The RPILNet also satisfies real-time operation requirements (>30 fps).

scholarly journals A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway

2020 ◽  
Vol 10 (24) ◽  
pp. 9059
Author(s):  
Meng Li ◽  
Yinghong Wen ◽  
Guodong Wang ◽  
Dan Zhang ◽  
Jinbao Zhang
Keyword(s):  
High Speed ◽  
Electromagnetic Compatibility ◽  
Graph Model ◽  
System Level ◽  
Train Control ◽  
Modeling Process ◽  
Time Operation ◽  
Measurement Results ◽  
Real Time Operation

The On-board Train Control System (OTCS) plays an important role in the real-time operation of the electric multiple units (EMU) in high-speed railway. The EMU is a complex system made up of various electrical and electronic equipment, so the interactions of the electromagnetic (EM) environment and OTCS are difficult to study, which leads to more challenges to analyze EM interference (EMI) events at the system level. To overcome this difficulty, this paper proposes the thought of a graph model to solve the problem. First, a framework is proposed to more clearly reflect the relationship between the EMC (Electromagnetic Compatibility) problem and network through a comparison with them. Second, a network theory-based model is presented to express the EMC elements for the OTCS in EMU. It decomposes the OTCS and EMU with EMC elements into edges and nodes of the network, which parameters are defined corresponding to EM sources, sensitive equipment, and coupling paths. Thus, each part could be modeled separately or together by calculation, simulation, or measurement, respectively, and the EMC problem could be represented by the paths from origin to destination in the network. Moreover, the modeling process was elucidated by the specific cases in OTCS and the validity of the proposed approach was verified by calculation and measurement results in the case study.

Vision-Controlled Robot Using Hand Pose Estimation without Marker Loading Requirement

2012 ◽  
Vol 162 ◽  
pp. 358-367
Author(s):  
Kiyoshi Hoshino
Keyword(s):  
Pose Estimation ◽  
High Speed ◽  
Visual Servoing ◽  
Hand Pose Estimation ◽  
Orthogonal Relationship ◽  
Time Operation ◽  
Real Time Operation ◽  
Hand Region ◽  
Detailed Search ◽  
Hand Pose

The author proposes a visual-servoing and vision-controlled robot. It claims no sensors installed or special control means used, instead of that a high-precision and high-speed 3D hand pose estimation permits real time operation with two cameras installed at positions of loosely orthogonal relationship, using one PC of the normal specifications. Two cameras have their own database. Once sequential hand images are recorded with these two high-speed cameras, the system first selects one database with bigger size of hand region in each recorded image. Second, a coarse screening is carried out according to the proportional information on the hand image which roughly correspond to wrist rotation, or thumb or finger extension. Third, a detailed search is performed for similarity among the selected candidates. The estimated results are transmitted to a robot so that the same motions of an operator is reconstructed in the robot without time delay.

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