Moving Object Detection Using the Genetic Algorithm for Real Times Transportation

Video surveillance, most commonly referred to as closed-circuit TV, is an industry over 30 years old and with a share of modifications in technology. To meet the requirements include: Better image quality, Reduction in costs, Size and scalability etc., video surveillance has experienced a number of technology shifts. For real-time traffic monitoring apps, we implement a process for the identification of objects. The suggested technique is a mixture of a GDSM, an enhanced version of the dynamic saliency map (DSM) and background subtraction. The experimental findings demonstrate the effective detection of moving objects by the suggested technique. Recent advances in vision technologies like distributed intelligent cameras have motivated scientists to create sophisticated apps for computer vision appropriate for embedded platforms. Simple and effective computer vision algorithms are needed in the integrated monitoring system with limited memory and computing resources.

Improved Hough Transform Applying in Mobile Robot Vision Navigation

This paper mainly discusses that in mobile robot vision navigation system, by using the improved Hough transform, we can improve the accuracy of line extraction and therefore avoid the image quality reduction caused by noise points. Considering the limitations of the standard Hough transform, we come up with a method with which we will accumulates the H (ρ, θ) through distributing the increment value, set a global threshold to shun the pointless measurements, eliminate the false lines by comparing θ difference between tow arbitrary lines, find the peaks by using rectangle window, and set a local threshold to eliminate false peaks. In this way, we can gain a method superior to the standard Hough transform which works better in extracting lines in application. The experiments show that this method can not only extract line features of geometric figure effectively in brief background, but also eliminate the iterative lines efficiently.