A Study and Analysis of Improved Binary Pattern Technique in Dynamic Images

Abstract

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.