A temporal-spatial background modeling of dynamic scenes

Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density estimator （G-KDE） that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal- spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content （NIC） is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.