A Semitransparency-Based Optical-Flow Method With a Point Trajectory Model for Particle-Like Video
This paper proposes a new semitransparency-based optical-flow model with a point trajectory (PT) model for particle-like video. Previous optical-flow models have used ranging from image brightness constancy to image brightness change models as constraints. However, two important issues remain unsolved. The first is how to track/match a semitransparent object with a very large displacement between frames. Such moving objects with different shapes and sizes in an outdoor scene move against a complicated background. Second, due to semitransparency, the image intensity between frames can also violate a previous image brightness- based optical-flow model. Thus, we propose a two-step optimization for the optical-flow estimation model for a moving semitransparent object, i.e., particle. In the first step, a rough optical flow between particles is estimated by a new alpha constancy constraint that is based on an image generation model of semitransparency. In the second step, the optical flow of a particle with a continuous trajectory in a definite temporal interval based on a PT model can be refined. Many experiments using various falling-snow and foggy scenes with multiple moving vehicles show the significant improvement of the optical flow compared with a previous optical-flow model.
semitransparency particle large displacement two-step optimization optical flow natural scene