An Improved Kernel Density Estimation Approach for Moving Objects Detection

Moving object detection based on monitoring video system is often a challenging problem. Specially to monitor traffic at both day and night, in different weather and illumination conditions and with changeable background. Kernel Density Estimation (KDE) model is an effective approach to judge background and foreground, however, typical KDE uses fixed parameters, such as bandwidths, threshold, etc. This paper proposes a detection algorithm based on an Improved Kernel Density Estimation (IKDE) model. The proper bandwidths, adaptive background sample learning array, and adaptive threshold, and an improved sample updating method for sample learning array are discussed as the fundamentals of the IKDE model. Furthermore, an algorithm for restraining light field disturbance at night in video scene is proposed. Video image series are evaluated through the algorithm, and moving object detection is conducted in three different scenes. Results show that the algorithm can help to achieve a promising high accuracy and robustness for detecting moving objects.