关键词: 智能制造, 智能交通, 运动目标检测, 光照变化

Abstract: Accurate target detection is the foundation of modern intelligent manufacturing,intelligent transportation and other fields.However,current classic visual foreground extraction methods struggle to meet real-time and accurate requirements in scenes with slow or sudden lighting changes.Therefore,an improved moving object detection method that combined Wronskian function with Visual Background extractor (ViBe) was proposed.The background model was built based on the consistency of temporal and spatial samples,and it was initialized using multiple frames with a specific time interval.Then,the Wronskian function was used to judge the linear correlation between the vector of current pixels and the vector of samples in the background model.The times of linear correlation was used to detect whether the current pixels belonged to moving or background pixels.Finally,the background model was updated using the detection results.Qualitative and quantitative experimental results on real-world scenes showed that the proposed method could effectively overcome the influence of multiple disturbances existing in illumination scenes to extract moving objects.It achieved a higher detection rate and more robust performance when compared with other state-of-the-art methods under slow or abrupt illumination change scenes.

Key words: intelligent manufacturing, intelligent transportation, moving object detection, illumination change