Energy and Environment Systems Laboratories, Nippon Telegraph and Telephone Corporation, Tokyo 180-8585, Japan.
IEEE Trans Image Process. 2012 Feb;21(2):441-50. doi: 10.1109/TIP.2011.2165220. Epub 2011 Aug 18.
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.
本文提出了一种新的基于半透明性的光流模型，该模型具有点状轨迹 (PT) 模型，用于粒子状视频。以前的光流模型使用的约束条件从图像亮度恒定性到图像亮度变化模型不等。然而，仍有两个重要问题尚未解决。第一个问题是如何跟踪/匹配帧间具有很大位移的半透明物体。在户外场景中，具有不同形状和大小的此类移动物体与复杂的背景相对运动。其次，由于半透明性，帧间的图像强度也可能违反以前基于图像亮度的光流模型。因此，我们针对运动半透明物体（即粒子）的光流估计模型提出了两步优化。在第一步中，通过基于半透明图像生成模型的新 alpha 恒常性约束来估计粒子之间的粗略光流。在第二步中，可以基于 PT 模型细化在确定的时间间隔内具有连续轨迹的粒子的光流。使用具有多个移动车辆的各种飘落雪和雾场景进行的许多实验表明，与以前的光流模型相比，光流有了显著的改进。
