scholarly journals High-Speed Tracking with Multi-kernel Correlation Filters

2018 ◽  
Author(s):  
Ming Tang ◽  
Bin Yu ◽  
Fan Zhang ◽  
Jinqiao Wang
Keyword(s):  
High Speed ◽  
Correlation Filters ◽  
Speed Tracking ◽  
Kernel Correlation

scholarly journals Design of Airport Obstacle-Free Zone Monitoring UAV System Based on Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2475
Author(s):  
Liang Wang ◽  
Jianliang Ai ◽  
Li Zhang ◽  
Zhenlin Xing
Keyword(s):  
Computer Vision ◽  
High Speed ◽  
Degrees Of Freedom ◽  
High Reliability ◽  
Low Cost ◽  
Pid Algorithm ◽  
Free Zone ◽  
Speed Tracking ◽  
Kernel Correlation ◽  
Tracking Technology

In recent years, a rising number of incidents between Unmanned Aerial Vehicles (UAVs) and planes have been reported at airports and airfields. A design scheme for an airport obstacle-free zone monitoring UAV system based on computer vision is proposed. The system integrates the functions of identification, tracking, and expelling and is mainly used for low-cost control of balloon airborne objects and small aircrafts. First, a quadcopter dynamic model and 2-Degrees of Freedom (2-DOF) Pan/Tilt/Zoom (PTZ) model are analyzed, and an attitude back-stepping controller based on disturbance compensation is designed. Second, a low and slow small-target self-identification and tracking technology is constructed against a complex environment. Based on the You Only Look Once (YOLO) and Kernel Correlation Filter (KCF) algorithms, an autonomous target recognition and high-speed tracking plan with great robustness and high reliability is designed. Third, a PTZ controller and automatic aiming strategy based on Anti-Windup Proportional Integral Derivative (PID) algorithm is designed, and a simplified, automatic-aiming expelling device, the environmentally friendly gel ball blaster, which features high speed and high accuracy, is built. The feasibility and stability of the project can be verified through prototype experiments.

scholarly journals High-Speed Tracking with Kernelized Correlation Filters

2015 ◽  
Vol 37 (3) ◽  
pp. 583-596 ◽  
Author(s):  
Joao F. Henriques ◽  
Rui Caseiro ◽  
Pedro Martins ◽  
Jorge Batista
Keyword(s):  
High Speed ◽  
Correlation Filters ◽  
Speed Tracking ◽  
Kernelized Correlation Filters

High-speed tracking with multi-templates correlation filters

2021 ◽  
Vol 30 (06) ◽  
Author(s):  
Bin Pu ◽  
Ke Xiang ◽  
Jiayu Ji ◽  
Xuanyin Wang
Keyword(s):  
High Speed ◽  
Correlation Filters ◽  
Speed Tracking

scholarly journals High-speed tracking based on multi-CF filters and attention mechanism

2019 ◽  
Author(s):  
Songyuan Fan ◽  
Rui Wang ◽  
Zhihao Wu ◽  
Seungmin Rho ◽  
Shaohui Liu ◽  
...  
Keyword(s):  
High Speed ◽  
Feature Maps ◽  
Correlation Filters ◽  
Convolutional Network ◽  
Speed Tracking ◽  
Training Samples ◽  
Long Time ◽  
Fast Motion ◽  
Evaluation Mechanism

Abstract Recently, correlation filters and deep convolutional network show good performance for visual tracking. Many real-time and high accuracy tracking algorithms are realized; however, there are still some challenges to build a robust tracker. In this paper, we present a novel tracking framework named multi-attention filter (MAF) to solve some challenges for tracking like object drift in a long time, lack of training samples and fast motion. Our framework consists of two components, a basic CNN network to extract feature maps and a set of classifiers to distinguish between the target and the background. First, to solve the problem of object drift in a long time, a simple but effective evaluation mechanism is proposed to the framework, the evaluation mechanism checks the tracking results and corrects it when needed. In addition, the results from different classifiers are fused to predict the object location according to intersection over union. Second, to overcome the lack of training samples, MAF stores some positive and negative samples in two queues, one named long-term queue and another named short-term queue. Third, to deal with fast motion of the target, attention mechanisms including channel attention and location attention are added to the tracker. In our experiments on the popular benchmarks including OTB-2013 and OTB-2015. MFA achieves state of the art among trackers, and as a correlation filter framework, MAF is very flexible and has great rooms for improvement and generalization.

scholarly journals Robust Visual Tracking via Constrained Multi-Kernel Correlation Filters

2020 ◽  
Vol 22 (11) ◽  
pp. 2820-2832 ◽  
Author(s):  
Bo Huang ◽  
Tingfa Xu ◽  
Shenwang Jiang ◽  
Yiwen Chen ◽  
Yu Bai
Keyword(s):  
Visual Tracking ◽  
Correlation Filters ◽  
Kernel Correlation

High-Speed Tracking of Murine Cardiac Stem Cells by Harmonic Nanodoublers

Small ◽  
2012 ◽  
Vol 8 (17) ◽  
pp. 2752-2756 ◽  
Author(s):  
Thibaud Magouroux ◽  
Jerome Extermann ◽  
Pernilla Hoffmann ◽  
Yannick Mugnier ◽  
Ronan Le Dantec ◽  
...  
Keyword(s):  
Stem Cells ◽  
High Speed ◽  
Cardiac Stem Cells ◽  
Speed Tracking

Nonlinear Model Predictive Control for Automatic Train Operation with Actuator Saturation and Speed Limit

2014 ◽  
Vol 678 ◽  
pp. 377-381
Author(s):  
Long Sheng Wang ◽  
Hong Ze Xu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
High Speed ◽  
Actuator Saturation ◽  
Tracking Error ◽  
Nonlinear Model Predictive Control ◽  
Speed Limit ◽  
Speed Tracking ◽  
Automatic Train Operation

This paper addresses a position and speed tracking problem for high-speed train automatic operation with actuator saturation and speed limit. A nonlinear model predictive control (NMPC) approach, which allows the explicit consideration of state and input constraints when formulating the problem and is shown to guarantee the stability of the closed-loop system by choosing a proper terminal cost and terminal constraints set, is proposed. In NMPC, a cost function penalizing both the train position and speed tracking error and the changes of tracking/braking forces will be minimized on-line. The effectiveness of the proposed approach is verified by numerical simulations.

Modeling magnetostrictive material for high-speed tracking

2011 ◽  
Vol 109 (7) ◽  
pp. 07B525 ◽  
Author(s):  
Oriano Bottauscio ◽  
Paolo E. Roccato ◽  
Mauro Zucca
Keyword(s):  
High Speed ◽  
Magnetostrictive Material ◽  
Speed Tracking
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