The position and attitude measurement of space object is a key problem in the field of real-time navigation, modern control and motion tracking. As a non-contact position and attitude estimation method, machine vision position and attitude estimation has the advantages of simple structure and convenient measurement. This paper presents a vision positioning system and method based on multiple reference markers. The camera moving along the object continuously collects images containing reference markers from the camera's field of view.The spatial position information of reference mark is determined in advance, and the position and direction of moving target are calculated according to location and attitude algorithm. The main contribution of this paper: first, a plurality of reference markers is arranged in the range of moving objects so as to enlarge the range of visual positioning; second, when more than one reference marker appears in the field of view, it is possible to improve the positioning accuracy by selecting the marker of the larger contour area or the marker of the distance closer to the imaging plane principal point; third, we use the decoder to transform the reference marker into digital number. This method can improve the robustness of the system.
This paper presents a novel algorithm named Ensemble Distance Metric Tracking (EDMT) for target tracking in infrared
imagery. Obtaining an appropriate distance metric function can significantly improve the performance of tracking
algorithms. There are two problems in distance metric choosing for object tracking. First, we can't find the data model
distribution beforehand for most tracking application. Second, the data model will change as both foreground and
background appearance undergoes complex changes with the target object moving from place to place. So the distance
metric function also needs to adapt dynamically during the tracking procedure. Most tracking applications are conducted
using a fixed distance metric function, which is determined beforehand. We propose a new algorithm that can learn and
update the distance metric dynamically, which is different from the conventional methods that use the predefined metric.
In our new EDMT algorithm, the ensemble distance metric function is learnt by weighted training with different distance
metrics on each feature element iteratively using the boosting learning method. The new distance metric function is
adopted in particle filter to compute the weights of each particle. The experimental results demonstrate the effectiveness
and robustness of our tracking algorithm in challenging infrared video sequences.
In this paper, we propose a novel general framework for target detection and tracking in infrared image sequences. An
integrated tracking system is described by this framework based on multiple models learning online. The relations
among each component of the tracking system are expressed distinctly. Furthermore, we emphasize that the main
components of the tracking system shouldn't be invariable. On the contrary, they should update dynamically. An
integrated tracking system is composed of six modules. The target appearance will change as the target object moves
from one place to another. So the object description also needs update dynamically in the tracking framework. At the
core of many approaches for object tracking is the metric or similarity measure used to determine the distance between
the target template and candidates. In the proposed tracking framework, the distance measure is learnt online and update
dynamically by the ensemble learning algorithm. Approaches on estimation of object tracking can be divided into two
groups: deterministic approaches and stochastic approaches. In our unified framework, the estimation approach is not
fixed, but adaptive. The observation model, motion model and number of particles can adapt to the changes of the
foreground and background. Our extensive experiments show that the presented algorithm performs robustly in a large
variety of infrared image sequences. The approach proposed in this paper has the potential to solve other sensor fusion
problems.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.