Motion Estimation from Target Tracking

1991 ◽  
pp. 445-458
Author(s):  
Juan Frau ◽  
Vicenç Llario ◽  
Joan Codina
Keyword(s):  
Motion Estimation ◽  
Target Tracking

Vision-based target tracking and motion estimation using a small UAV

2010 ◽  
Author(s):  
Zhiyuan Li ◽  
Naira Hovakimyan ◽  
Vladimir Dobrokhodov ◽  
Isaac Kaminer
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Small Uav

Software-based robust global motion estimation for real-time video target tracking

2011 ◽  
Author(s):  
Chenhui Yang ◽  
Hongwei Mao ◽  
Glen P. Abousleman ◽  
Jennie Si
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Real Time ◽  
Global Motion ◽  
Global Motion Estimation ◽  
Video Target Tracking

scholarly journals Vision-based dynamic target trajectory and ego-motion estimation using incremental light bundle adjustment

2018 ◽  
Vol 10 (2) ◽  
pp. 157-170 ◽  
Author(s):  
Michael Chojnacki ◽  
Vadim Indelman
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Moving Objects ◽  
3D Structure ◽  
Computational Effort ◽  
Bundle Adjustment ◽  
Computational Time ◽  
Computationally Efficient ◽  
Dynamic Target ◽  
Real Imagery

This paper presents a vision-based, computationally efficient method for simultaneous robot motion estimation and dynamic target tracking while operating in GPS-denied unknown or uncertain environments. While numerous vision-based approaches are able to achieve simultaneous ego-motion estimation along with detection and tracking of moving objects, many of them require performing a bundle adjustment optimization, which involves the estimation of the 3D points observed in the process. One of the main concerns in robotics applications is the computational effort required to sustain extended operation. Considering applications for which the primary interest is highly accurate online navigation rather than mapping, the number of involved variables can be considerably reduced by avoiding the explicit 3D structure reconstruction and consequently save processing time. We take advantage of the light bundle adjustment method, which allows for ego-motion calculation without the need for 3D points online reconstruction, and thus, to significantly reduce computational time compared to bundle adjustment. The proposed method integrates the target tracking problem into the light bundle adjustment framework, yielding a simultaneous ego-motion estimation and tracking process, in which the target is the only explicitly online reconstructed 3D point. Our approach is compared to bundle adjustment with target tracking in terms of accuracy and computational complexity, using simulated aerial scenarios and real-imagery experiments.

Digital Image Stabilization Algorithm Based on Mean Shift Corner Tracking

2011 ◽  
Vol 130-134 ◽  
pp. 3155-3157
Author(s):  
Jian Peng ◽  
Qing Min ◽  
Zhi Qiang Xu
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Digital Image ◽  
Motion Compensation ◽  
Mean Shift ◽  
Image Stabilization ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
The Core ◽  
Core Technology

The core technology of electronic ois is the motion estimation techniques, such as, BPM, RPM, FTA ,PA, BM and so on[1]. Based on the excellent tracking performance of Mean Shift algorithm in complicated scene, this paper proposed a target tracking method based on corner, and then work on motion compensation and motion estimation.

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