Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments

In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

https://www.mdpi.com/1099-4300/21/9/902/pdf

Optimization of Big Data Scheduling in Social Networks

An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.

/pdf/overview-and-methods-of-correlation-filter-algorithms-in-4urxd2fbtj.pdf

Overview and methods of correlation filter algorithms in object tracking

The new Boussinesq-type model in a fractal domain is derived based on the formulation of the local fractional derivative The novel traveling wave transform of the non-differentiable type is adopted to convert the local fractional Boussinesq equation into a nonlinear local fractional ODE The exact traveling wave solution is also obtained with aid of the non-differentiable graph The proposed method, involving the fractal special functions, is efficient for finding the exact solutions of the nonlinear PDEs in fractal domains

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X17400060

Exact traveling-wave solution for local fractional boussinesq equation in fractal domain

Reliability of response region: A novel mechanism in visual tracking by edge computing for IIoT environments

With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2016.0862

Digital image watermarking method based on DCT and fractal encoding

Fractal encoding method becomes an effective image compression method because of its high compression ratio and short decompressing time. But one problem of known fractal compression method is its high computational complexity and consequent long compressing time. To address this issue, in this paper, distance clustering in high dimensional sphere surface is applied to speed up the fractal compression method. Firstly, as a preprocessing strategy, an image is divided into blocks, which are mapped on high dimensional sphere surface. Secondly, a novel image matching method is presented based on distance clustering on high dimensional sphere surface. Then, the correctness and effectiveness properties of the mentioned method are analyzed. Finally, experimental results validate the positive performance gain of the method.

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X17400047

A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface

Recently, computer vision and multimedia understanding become important research domains in computer science. Meanwhile, visual tracking of moving target, one of most important application in computer vision, becomes a highlight today. So, this paper reviews research and technology in this domain. First, background and application of visual tracking is introduced. Then, visual tracking methods are classified by different thinking and technologies. Their positiveness, negativeness and improvement are analyzed deeply. Finally, difficulty in this domain is summarized and future prospect of related fields is presented.

A review of visual moving target tracking

Visual attention feature (VAF) : A novel strategy for visual tracking based on cloud platform in intelligent surveillance systems

/pdf/fractal-generation-method-based-on-asymptote-family-of-4gj6t28ely.pdf

Zheng Pan

Papers

Digital image watermarking method based on DCT and fractal encoding

A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface

A review of visual moving target tracking

Visual attention feature (VAF) : A novel strategy for visual tracking based on cloud platform in intelligent surveillance systems

Fractal generation method based on asymptote family of generalized Mandelbrot set and its application