Performance evaluation of fuzzy 2-partition entropy and big bang big crunch optimization based object detection and tracking approach

Abstract

Background subtraction (BS) is one of most commonly used methods for detection of moving objects in videos that works by subtracting current frame from a background frame. Effective background modeling and threshold plays a crucial role in BS and can govern accuracy and preciseness of object boundaries. This paper proposes a fuzzy entropy based approach modified BS algorithm for moving object detection with Kalman tracker. The standard BS method has been enhanced using concept of fuzzy 2-partition entropy and big bang big crunch optimization (BBBCO). BBBCO has been used to enhance standard BS algorithm for extracting various parameters required in BS algorithm by framing the problem of parameters detection as optimization problem which is solved using concept of fuzzy partition entropy. The proposed algorithm generates optimal threshold values along with various other measures for background modeling. The detected objects are further tracked using Kalman filter based tracker. The evaluation of proposed method has been done on videos from benchmark datasets and statistical parameters have been calculated. The method is also compared with standard BS and another recent study in the field. The results show promise of the proposed method.