Abstract
Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.
Highlights
The most imperative characteristic of an intelligent vision based inspection system is background subtraction, which is considered to be a primitive step for object recognition and tracking
The estimated threshold values, from both the cases are considered and are given as input to the model Generalized Rayleigh Distribution proposed in section III of the article
In case of Adaptive frame difference fused method, the choice of the adaptive threshold value is based on the difference value obtained by subtracting the background reference frame from the current frame and these values are fused to get a unique threshold value
Summary
The most imperative characteristic of an intelligent vision based inspection system is background subtraction, which is considered to be a primitive step for object recognition and tracking. Pixel by pixel comparison is practiced for either detection or tracking with a predefined object dataset This procedure of searching and comparing against each pixel requires a huge computational time and as an improvement to this approach, background subtraction methods are coined for the optimization of both search and computational time. Most of the background modelling techniques need to combat the challenges due to dynamic or non-static backgrounds, unexpected or steady lighting changes; motion in the object and shade, Background modelling methods should intelligently overcome such issues. Many models are presented in the literature [1,2,3,4,5,6,7,8,9,10,11], [13], [15], [16], [17], [18]
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