Moving Object Detection Using Energy Model And Particle Filter For Dynamic Scene

We proposed an algorithm that uses an energy model with smoothness assumption to identify a moving object by using optical flow, and uses a particle filter with a proposed observation and dynamic model to track the object. The algorithm is based on the assumption that the dominant motion is background flow and that foreground flow is separated from the background flow. The energy model provides the initial label foreground object well, and minimizes the number of noise pixels that are included in the bounding box. The tracking part uses HOG-3 as an observation model, and optical flow as the dynamic model. This combination of models improves the accuracy of tracking results. In experiments on challenging data set that have no initial labels, the algorithm achieved meaningful accuracy compared to a state-of-the-art technique that needs initial labels.