8/22/2023 0 Comments Flow frames![]() Configuring OpenCV to read a video and setting up parameters 3. We will be writing all of the code in this Python file. Next, open sparse-starter.py with your text editor. Now, clone the tutorial repository by running: ![]() ![]() If you do not already have OpenCV installed, open Terminal and run: We will be using the Lucas-Kanade method with OpenCV, an open source library of computer vision algorithms, for implementation. There are various implementations of sparse optical flow, including the Lucas–Kanade method, the Horn–Schunck method, the Buxton–Buxton method, and more. The extracted features are passed in the optical flow function from frame to frame to ensure that the same points are being tracked. interesting features such as edges and corners) to track its velocity vectors (motion). Sparse optical flow selects a sparse feature set of pixels (e.g. Left: Sparse Optical Flow - track a few "feature" pixels Right: Dense Optical Flow - estimate the flow of all pixels in the image. As you would've guessed, Dense optical flow has higher accuracy at the cost of being slow/computationally expensive. Sparse optical flow gives the flow vectors of some "interesting features" (say few pixels depicting the edges or corners of an object) within the frame while Dense optical flow, which gives the flow vectors of the entire frame (all pixels) - up to one flow vector per pixel. In this tutorial, we will learn what Optical Flow is, how to implement its two main variants (sparse and dense), and also get a big picture of more recent approaches involving deep learning and promising future directions. Or, alternatively, what if we require information on human pose relationships between consecutive frames to recognize human actions such as archery, baseball, and basketball? Various action classifications Classifying actions with optical flow However, what if we do need the relationships between consecutive frames, for example, we want to track the motion of vehicles across frames to estimate its current velocity and predict its position in the next frame? Sparse optical flow of traffic (Each arrow points in the direction of predicted flow of the corresponding pixel). In other words, they re-evaluate each frame independently, as if they are completely unrelated images, for each run. However, for processing real-time video input, most implementations of these techniques only address relationships of objects within the same frame \((x, y)\) disregarding time information \((t)\). ![]() Recent breakthroughs in computer vision research have allowed machines to perceive its surrounding world through techniques such as object detection for detecting instances of objects belonging to a certain class and semantic segmentation for pixel-wise classification. We also briefly discuss more recent approaches using deep learning and promising future directions. In this tutorial, we dive into the fundamentals of Optical Flow, look at some of its applications and implement its two main variants (sparse and dense). ![]()
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