Adaptive ViBe background model for vehicle detection
Chengyi Pan, Zhou Zhu, Liangwei Jiang, Min Wang, Xiaobo Lu · 2017
Background extraction is an important step in vehicle detection. In the actual scene, change of illumination will lead to a tremendous background change. It is necessary to update the background model reasonably and effectively as the illumination changes. In order to solve this problem, this paper proposes an adaptive ViBe background model. Firstly, two kinds of vehicle detection errors and their corresponding error function are defined. Then, according to the range of these two kinds of errors, a set of reasonable evaluation conditions are determined to adjust the unreasonable threshold value, which guarantees the adaptive updation of the background model. Experiments in real scenarios show that the adaptive ViBe background model has better vehicle detection accuracy than the mixed Gaussian model, the codebook model and the fixed threshold ViBe model.