Comparative study of illumination-invariant foreground detection

Abstract

Foreground detection plays a vital role in finding the moving objects of a scene. For the last two decades, many methods were introduced to tackle the issue of illumination variation in foreground detection. In this article, we proposed a method to segment moving objects under abrupt illumination change and analyzed the merits and demerits of the proposed method with seven other algorithms commonly used for illumination-invariant foreground detection. The proposed method calculates the entropy of the video scene to determine the level of illumination change occurred and select the update model based on the difference in entropy values. Benchmark datasets possessing different challenging illumination conditions are used to analyze the efficiency of the foreground detection algorithms. Experimental studies demonstrate the performance of the proposed algorithm with several algorithms under various illumination conditions and its low time complexity.