Mr. Zhenyu Wang Profile

Zhenyu Wang

at North China Electric Power Univ

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Publications (3)

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Proceedings Article | 15 November 2017 Paper

Navigation system based on machine vision of multiple reference markers

Xiaopeng Su, Wenbo Dong, Zhenyu Wang, Yuanyuan Zhou

Proceedings Volume 10605, 106053V (2017) https://doi.org/10.1117/12.2295880

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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.

Proceedings Article | 3 April 2010 Paper

Automatic target tracking in infrared image sequences using ensemble distance metric

Zhenyu Wang, Guotian Yang

Proceedings Volume 7651, 76510N (2010) https://doi.org/10.1117/12.855390

KEYWORDS: Detection and tracking algorithms, Infrared radiation, Infrared imaging, Infrared search and track, Automatic tracking, Data modeling, Particle filters, Particles, Thermography, Video

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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.

Proceedings Article | 6 August 2009 Paper

A new unified framework for object detection and tracking in infrared imagery

Zhenyu Wang, Jie Chen, Yan Bai

Proceedings Volume 7383, 73832X (2009) https://doi.org/10.1117/12.829883

KEYWORDS: Infrared imaging, Infrared radiation, Infrared detectors, Infrared search and track, Target detection, Detection and tracking algorithms, Distance measurement, Image fusion, System integration, Motion models

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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.

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