BBBCO and fuzzy entropy based modified background subtraction algorithm for object detection in videos

摘要

Background subtraction (BS) is one of the most commonly used methods for detecting moving objects in videos. In this task, moving objectpixels are extracted by subtracting the current frame from a background frame. The obtained difference is compared against a threshold value to classify pixels as belonging to the foreground or background regions. The threshold plays a crucial role in this categorization and can impact the accuracy and preciseness of the object boundaries obtained by the BS algorithm. This paper proposes an approach for enhancing and optimizing the performance of the standard BS algorithm. This approach uses the concept of fuzzy 2-partition entropy and Big Bang-Big Crunch Optimization (BBBCO). BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO enhances the standard BS algorithm by framing the problem of parameter detection for BS as an optimization problem, which is solved using the concept of fuzzy 2-partition entropy. The proposed method is evaluated using videos from benchmark datasets and a number of statistical metrics. The method is also compared with standard BS and another recently proposed method. The results show the promise of the proposed method.