Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.

In this paper, we propose a simple anaglyph 3D stereo generation algorithm from 2D video sequence with a monocular camera. In our novel approach, we employ camera pose estimation method to directly generate stereoscopic 3D from 2D video without building depth map explicitly. Our cost-effective method is suitable for arbitrary real-world video sequence and produces smooth results. We use image stitching based on plane correspondence using fundamental matrix. To this end, we also demonstrate that correspondence plane image stitching based on Homography matrix only cannot generate a better result. Furthermore, we utilize the structure-from-motion (with fundamental matrix) based reconstructed camera pose model to accomplish visual anaglyph 3D illusion. The anaglyph result is visualized by a contour based yellow-blue 3D color code. The proposed approach demonstrates a very good performance for most of the video sequences in the user study.

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area.