Available car parking space detection from webcam by using adaptive mixing features

This paper presents a robust approach for detection of available car parking spaces. With low quality of video camera as webcam and dynamic change of light around the car parking, it is hard to accurately detect or recognize the cars. Moreover the proposed appearance-based approach is efficient than recognition-based approach because it do not need to learn a huge of multi-view objects. In this paper, we propose adaptive background model-based object detection with dynamic mixing features of masked-area and edge orientation histogram (EOH) density. The average variance of variance of intensity change for dynamic background model is used to change ratio of mixing features dynamically. The masked-area density is density of predefined area of a parking slot that is weighted by Gaussian mask to robust density computation and the edge orientation histogram (EOH) density is density of the EOH in the predefined area that can be used under low contrast image as night scene. The experiments are performed both in simulation model and real scenes. The results show the proposed approach can handle dynamic change of light efficiently.