Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm
نویسنده
چکیده
1 Problem Statement Let I and J be two 2D grayscaled images. The two quantities I(x) = I(x, y) and J(x) = J(x, y) are then the grayscale value of the two images are the location x = [x y] , where x and y are the two pixel coordinates of a generic image point x. The image I will sometimes be referenced as the first image, and the image J as the second image. For practical issues, the images I and J are discret function (or arrays), and the upper left corner pixel coordinate vector is [0 0] . Let nx and ny be the width and height of the two images. Then the lower right pixel coordinate vector is [nx − 1 ny − 1] . Consider an image point u = [ux uy] on the first image I. The goal of feature tracking is to find the location v = u + d = [ux+dx uy +dy] on the second image J such as I(u) and J(v) are “similar”. The vector d = [dx dy] is the image velocity at x, also known as the optical flow at x. Because of the aperture problem, it is essential to define the notion of similarity in a 2D neighborhood sense. Let ωx and ωy two integers. We define the image velocity d as being the vector that minimizes the residual function defined as follows:
منابع مشابه
Performance Evaluation of Feature Detection for Local Optical Flow Tracking
Due to its high computational efficiency the Kanade Lucas Tomasi feature tracker is still widely accepted and a utilized method to compute sparse motion fields or trajectories in video sequences. This method is made up of a Good Feature To Track feature detection and a pyramidal Lucas Kanade feature tracking algorithm. It is well known that the Good Feature To Track takes into account the Apert...
متن کاملیک الگوریتم ردیابی خودرو مبتنی بر ویژگی با استفاده از گروهبندی سلسله مراتبی ادغام و تقسیم
Vehicle tracking is an important issue in Intelligence Transportation Systems (ITS) to estimate the location of vehicle in the next frame. In this paper, a feature-based vehicle tracking algorithm using Kanade-Lucas-Tomasi (KLT) feature tracker is developed. In this algorithm, a merge and split-based hierarchical two-stage grouping algorithm is proposed to represent vehicles from the tracked fe...
متن کاملTracking Features with Large Motion
This paper addresses feature tracking when frame-toframe motion is too large that the popular pyramidal Kanade-Lucas-Tomasi (KLT) feature tracker does not work. To solve this problem, we estimate the motion at the deepest pyramid level by matching the horizontal (and vertical) characteristic curves of the consecutive images. To compute the motion estimates efficiently and effectively, we use dy...
متن کاملConcurrent Tracking of Inliers and Outliers
In object tracking, outlier is one of primary factors which degrade performance of image-based tracking algorithms. In this respect, therefore, most of the existing methods simply discard detected outliers and pay little or no attention to employing them as an important source of information for motion estimation. We consider outliers as important as inliers for object tracking and propose a mo...
متن کاملVisual Servoing using Fuzzy Controllers on an Unmanned Aerial Vehicle
This paper presents an implementation of three Fuzzy Logic controllers working in parallel onboard a UAV, two for a pan-tilt camera platform and the third for control the yaw of the helicopter. This implementation uses a Lucas-Kanade tracker algorithm with a pyramidal optical flow implementation, which gives information to follow statics and moving objects, besides the UAV vibrations and moveme...
متن کاملUncertainty Estimation for KLT Tracking
The Kanade-Lucas-Tomasi tracker (KLT) is commonly used for tracking feature points due to its excellent speed and reasonable accuracy. It is a standard algorithm in applications such as video stabilization, image mosaicing, egomotion estimation, structure from motion and Simultaneous Localization and Mapping (SLAM). However, our understanding of errors in the output of KLT tracking is incomplet...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2000