Detection And Tracking Of Moving Objects using Particle Filter

Motion detection is the first essential process in the extraction of information regarding moving objects and makes use of stabilization in functional areas such as tracking, classification, recognition, and so on. In this paper, high - quality moving object detection is determined by using nonparametric modeling. The background is modeled by using the combination of chromaticity and gradients; it reduces the influence of shadows and reflected light. The foreground model combines this information and spatial information. Particle filter is introduced update the spatial information. The detection results produced by the partic le filter is analysed through visual inspection and for accuracy, along with comparisons to the results produced by other state - of - the - art methods