Visual Tracking based on Cooperative model

AbstractIn this paper, we propose a cooperative model combined the multi-task reverse sparse representation model (MTRSR) and the AdaBoost classifier, which were used to cope with the disturbing of target gradient information caused by motion blur or target serious occlusion, and a descriptive dictionary were used to estimate the weights of each candidates. First, we use the MTRSR model to get the blur kernel which were used to get the blur target template set, meanwhile the confidence of the candidates is also obtained by the reconstruction error. Then we use the HOG features of the target templates to get the descriptive dictionary to calculate the weights of the candidates, and a AdaBoost classifier is used to calculate the confidences of all candidates. Finally, the best target is retrieved by the sum of production of weight value and the two confidences. The experimental data show that the proposed algorithm can fully cope with the target’s information change which were caused by motion blur and target occlusion in the complex scene, and our algorithm can further improve the accuracy and robustness in visual tracking.Abstract In this paper, we propose a cooperative model combined the multi-task reverse sparse representation model (MTRSR) and the AdaBoost classifier, which were used to cope with the disturbing of target gradient information caused by motion blur or target serious occlusion, and a descriptive dictionary were used to estimate the weights of each candidates. First, we use the MTRSR model to get the blur kernel which were used to get the blur target template set, meanwhile the confidence of the candidates is also obtained by the reconstruction error. Then we use the HOG features of the target templates to get the descriptive dictionary to calculate the weights of the candidates, and a AdaBoost classifier is used to calculate the confidences of all candidates. Finally, the best target is retrieved by the sum of production of weight value and the two confidences. The experimental data show that the proposed algorithm can fully cope with the target’s information change which were caused by motion blur and target occlusion in the complex scene, and our algorithm can further improve the accuracy and robustness in visual tracking

Overview of Digital Image Restoration

Image restoration is an image processing technology with great practical value in the field of computer vision. It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair. This article firstly classify and summarize image restoration algorithms, and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation, based on the texture of image restoration and based on deep learning, then make the brief analysis of digital image restoration of subjective and objective evaluation method, and briefly summarize application of digital image restoration technique in the future and prospects, provide direction for the research on image after repair

Review on Video Object Tracking Based on Deep Learning

Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving object tracking algorithm in this paper. In this paper, the existing deep tracking-based target tracking algorithms are classified and sorted out. Based on the previous knowledge and my own understanding, several solutions are proposed for the existing methods. In addition, the existing deep learning target tracking method is still difficult to meet the requirements of real-time, how to design the network and tracking process to achieve speed and effect improvement, there is still a lot of research space

Investigation on microstructure and its transformation mechanisms of B2O3-SiO2-Al2O3-CaO brazing flux system

AbstractThe B2O3-SiO2-Al2O3-CaO brazing fluxes and slags were investigated by using X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The microstructure of the fluxes and slags and its transformation mechanism during the brazing process were investigated, especially the effect of ratio of B2O3to SiO2(B2O3/SiO2) on the microstructural transformation was analyzed. The results show that the structure units of the fluxes and slags are [BO4], [BO3], [SiO4], [AlO4] and [AlO6], and the network structure is a silicon-boron network structure. The O in the slags consist of bridged oxygen, non-bridged oxygen and free oxygen. During the brazing process, part of the [BO4] in slag combined with silica-oxygen network to form Si-O-B structure, which contribute to the network structure of slag, and another part of the [BO4] was transformed to [BO3]. The increase of (B2O3/SiO2) contribute to the transformation of [BO4] to [BO3], and more B2O3 take part in the interface reaction with the increase of (B2O3/SiO2). Therefore, the increase of (B2O3/SiO2) leads to the decrease in the viscosity of the slag, which is beneficial to the spreading behavior during the brazing process.</jats:p

Investigation on microstructure and its transformation mechanisms of B2O3-SiO2-Al2O3-CaO brazing flux system

The B2O3-SiO2-Al2O3-CaO brazing fluxes and slags were investigated by using X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR). The microstructure of the fluxes and slags and its transformation mechanism during the brazing process were investigated, especially the effect of ratio of B2O3to SiO2(B2O3/SiO2) on the microstructural transformation was analyzed. The results show that the structure units of the fluxes and slags are [BO4], [BO3], [SiO4], [AlO4] and [AlO6], and the network structure is a silicon-boron network structure. The O in the slags consist of bridged oxygen, non-bridged oxygen and free oxygen. During the brazing process, part of the [BO4] in slag combined with silica-oxygen network to form Si-O-B structure, which contribute to the network structure of slag, and another part of the [BO4] was transformed to [BO3]. The increase of (B2O3/SiO2) contribute to the transformation of [BO4] to [BO3], and more B2O3 take part in the interface reaction with the increase of (B2O3/SiO2). Therefore, the increase of (B2O3/SiO2) leads to the decrease in the viscosity of the slag, which is beneficial to the spreading behavior during the brazing process