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Visual Tracking Based on Reversed Sparse Representation
Wenhui Dong 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5
In this paper, we propose a fast and robust tracking method based on reversed sparse representation. Be different from other sparse representation based visual tracking methods, the target template is sparsely represented by the candidate particles which are gotten by particle filter. In order to improve the robustness of the method, we use a target template set. Meanwhile, a two level competition mechanism is also introduced. In the first level, each target template is sparsely represented and all the candidate particles compete with each other by a similarity calculation, which is based on sparse coefficients. Then, the winners construct a target candidate set. In the second level, all the target candidates in the target candidate set compete with each other and the one which is the most similar to the template set is considered as the target. In addition, a template set update strategy is proposed to adapt the appearance variations of the target. Experimental results on challenging benchmark video sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
Target Handoff with Appearance Model Inheriting and Learning
Wenhui Dong,Peishu Qu 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5
We address the issue of continuous tracking of the target in an environment covered by multiple cameras. In such a scenario, target handoff is a key problem. In this paper, we propose a novel target handoff method based on appearance model inheriting and learning. The appearance model is initially learned by sparse representation using the tracking results in the first camera. The next camera inherits the appearance model for target handoff and updates it after getting the whole tracking results. Then, the appearance model is transferred to the following camera. By the appearance model inheriting and learning, the appearance model can describe the target more and more precisely, which will make the target handoff more accurately and effectively. We also demonstrate the performance of our method on several video surveillance sequences.
Topology Learning of Non-overlapping Multi-camera Network
Xiaolin Li,Wenhui Dong,Faliang Chang,Peishu Qu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.11
We focus on the issue of learning the topology of the non-overlapping multi-camera network, which includes recovering the nodes (entry and exit zones), transition time distribution and links. Firstly, the nodes associated with each camera view are identified using clustering method. Then, transition time distribution is modeled as a Gaussian distribution and is computed by accumulated cross correlation and Gaussian fitting. Finally, the mutual information is used to refine the possible links and the topology is recovered. Experimental results on simulated data and real scene demonstrate the effectiveness of the proposed method.
Face Recognition based on Weber Symmetrical Local Graph Structure
( Jucheng Yang ),( Lingchao Zhang ),( Yuan Wang ),( Tingting Zhao ),( Wenhui Sun ),( Dong Sun Park ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.4
Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the 5 × 5 neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.
Target alignment method of inertial confinement fusion facility based on position estimation
Lin Weiheng,Zhu Jianqiang,Liu Zhigang,Pang Xiangyang,Zhou Yang,Cui Wenhui,Dong Ziming 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.10
Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments’ results, thereby improving the success rate of the experiments.