# Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios
## 1 Introduction  
Optical flow estimation still has potential and room for development, compared to other relatively mature vision tasks, such as image classification, segmentation, and object detection. Thanks to the high sensitivity of optical flow to pixels in motion, it is very promising in some complex application scenarios, such as motion capture, pose estimation, video interpolation, and even autonomous driving. Many years ago, [PERSON] and [PERSON] [1] introduced the concept of optical flow. Since then, researchers have been working on how to more accurately match the features or pixels in consecutive \"two-frame\" images, from the traditional optical flow computing method that [PERSON] and [PERSON] [2] proposed in 1981 to the current optical flow estimation algorithms based on deep learning. Although this approach to designing algorithms based on optical flow definitions is more in line with traditional theory and common sense, the working mechanism of neural networks is not exactly the same as traditional algorithms. The current state-of-the-art methods of optical flow estimation all follow the traditional input structure, which only extracts features from images of two consecutive frames. Even though they have made big progress on both AEPE (Average End-Point Error) metrics and time cost, an input structure like this ignores the influence of motion continuity, and the result will also be limited by losing such dynamic information. In summary, some basic qualities of motion, such as continuity and inertia, should not be neglected when estimating the motion state of any object. Humans, on the other hand, are able to observe an object's continuous motion, gather information from the previousmotion state, and swiftly build a plausible motion model to predict the object's next moving step. This point is more valuable in ball games with high-speed movement, where well-trained athletes can accurately predict the position of the ball at the next moment through the trajectory and movement trend of various balls. Achieving such an effect requires not only the spatial information of the motion but also the temporal information of it. In order to build a model for capturing these abstracted motion features, the deep learning method once again caught our attention because it can extract potential features and learn from them autonomously. Moreover, we know that, for deep neural networks, more effective auxiliary information usually contributes positively to the forward-backward process. Different pixels (or blocks of pixels) in images from consecutive time nodes may move in completely different states (including speeds, accelerations, etc.) within the same frame. These differences come from multiple sources: different sizes of objects, moving states, occluded or not, and different topological distances. Therefore, based on the existing optical flow estimation architecture, this paper designed a novel dynamic predicting module containing temporal information to predict and update the optical flow trends of all pixels. By using this module, optical flow estimation can be more consistent with the moving patterns of objects when processing an image sequence. The main contributions of this paper are as follows: compared with the input structure of the existing optical flow estimation methods, we propose to use three image frames (\(f_{t-2},f_{t-1},f_{t}\)) as input instead of two image frames (\(f_{t-1},f_{t}\)). We defined the features from the backbone output as optical flow frames (\(\Gamma_{t-1},\Gamma_{t}\)) and proved that using the dynamic prediction module, which is implemented by using consecutive optical flow frames as input, is more beneficial for optical flow estimation, especially for scenarios with fast-moving targets. We demonstrated that adding temporal information supplemented by a specially designed cost function to the optical flow estimating process did improve the accuracy.