1.Optical Flow 2.LogPolar Transform 3.Inertial Sensor 4.Corner Detection 5. Feature Tracking 6.Lines.

Slides:

Advertisements

Similar presentations

Distinctive Image Features from Scale-Invariant Keypoints

Advertisements

C280, Computer Vision Prof. Trevor Darrell Lecture 2: Image Formation.

Dynamic Occlusion Analysis in Optical Flow Fields

Distinctive Image Features from Scale- Invariant Keypoints Mohammad-Amin Ahantab Technische Universität München, Germany.

Two-view geometry.

Nick Hirsch: Progress Report. Track features from frame to frame using KLT Calculate their optical flow. Determine Focus of Expansion The center that.

A New Block Based Motion Estimation with True Region Motion Field Jozef Huska & Peter Kulla EUROCON 2007 The International Conference on “Computer as a.

Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany.

Active Calibration of Cameras: Theory and Implementation Anup Basu Sung Huh CPSC 643 Individual Presentation II March 4 th,

Motion Tracking. Image Processing and Computer Vision: 82 Introduction Finding how objects have moved in an image sequence Movement in space Movement.

1 Robust Video Stabilization Based on Particle Filter Tracking of Projected Camera Motion (IEEE 2009) Junlan Yang University of Illinois,Chicago.

Motion Detection And Analysis Michael Knowles Tuesday 13 th January 2004.

Probabilistic video stabilization using Kalman filtering and mosaicking.

Computing motion between images

CSc83029 – 3-D Computer Vision/ Ioannis Stamos 3-D Computational Vision CSc Optical Flow & Motion The Factorization Method.

Object Detection and Tracking Mike Knowles 11 th January 2005

Visual Odometry for Ground Vehicle Applications David Nister, Oleg Naroditsky, James Bergen Sarnoff Corporation, CN5300 Princeton, NJ CPSC 643, Presentation.

Augmented Reality: Object Tracking and Active Appearance Model

Optical Flow Estimation

CS223b, Jana Kosecka Rigid Body Motion and Image Formation.

3D Rigid/Nonrigid RegistrationRegistration 1)Known features, correspondences, transformation model – feature basedfeature based 2)Specific motion type,

Camera parameters Extrinisic parameters define location and orientation of camera reference frame with respect to world frame Intrinsic parameters define.

Motion from normal flow. Optical flow difficulties The aperture problemDepth discontinuities.

EE392J Final Project, March 20, Multiple Camera Object Tracking Helmy Eltoukhy and Khaled Salama.

Automatic Camera Calibration

Cloud Imagery and Motion Mark Anderson, Scott Cornelsen, and Tom Wilkerson Space Dynamics Laboratory Utah State University, Logan, UT

Human-Computer Interaction Human-Computer Interaction Tracking Hanyang University Jong-Il Park.

1 Computational Vision CSCI 363, Fall 2012 Lecture 31 Heading Models.

Geometric Camera Models

Motion Segmentation By Hadas Shahar (and John Y.A.Wang, and Edward H. Adelson, and Wikipedia and YouTube) 1.

Plenoptic Modeling: An Image-Based Rendering System Leonard McMillan & Gary Bishop SIGGRAPH 1995 presented by Dave Edwards 10/12/2000.

December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.

Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #16.

Geometry of Multiple Views

Pyramidal Implementation of Lucas Kanade Feature Tracker Jia Huang Xiaoyan Liu Han Xin Yizhen Tan.

3D Imaging Motion.

CS332 Visual Processing Department of Computer Science Wellesley College Analysis of Motion Recovering observer motion.

Two-view geometry. Epipolar Plane – plane containing baseline (1D family) Epipoles = intersections of baseline with image planes = projections of the.

Optical Flow. Distribution of apparent velocities of movement of brightness pattern in an image.

1 Motion Analysis using Optical flow CIS601 Longin Jan Latecki Fall 2003 CIS Dept of Temple University.

Distinctive Image Features from Scale-Invariant Keypoints David Lowe Presented by Tony X. Han March 11, 2008.

Segmentation of Vehicles in Traffic Video Tun-Yu Chiang Wilson Lau.

Copyright © 2012 Elsevier Inc. All rights reserved.. Chapter 19 Motion.

Course14 Dynamic Vision. Biological vision can cope with changing world Moving and changing objects Change illumination Change View-point.

Presented by: Idan Aharoni

3D Reconstruction Using Image Sequence

CS 376b Introduction to Computer Vision 03 / 31 / 2008 Instructor: Michael Eckmann.

Wire Detection Version 2 Joshua Candamo Friday, February 29, 2008.

Motion / Optical Flow II Estimation of Motion Field Avneesh Sud.

" Fitting methods with direct convolution for shear measurements." STEP Workshop August 2007 M. Shmakova.

MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.

MOTION Model. Road Map Motion Model Non Parametric Motion Field : Algorithms 1.Optical flow field estimation. 2.Block based motion estimation. 3.Pel –recursive.

Motion tracking TEAM D, Project 11: Laura Gui - Timisoara Calin Garboni - Timisoara Peter Horvath - Szeged Peter Kovacs - Debrecen.

Instantaneous Geo-location of Multiple Targets from Monocular Airborne Video.

Signal and Image Processing Lab

From: An Automated Reference Frame Selection (ARFS) Algorithm for Cone Imaging with Adaptive Optics Scanning Light Ophthalmoscopy Trans. Vis. Sci. Tech..

Motion and Optical Flow

Reconstruction For Rendering distribution Effect

Vision Based Motion Estimation for UAV Landing

Florian Shkurti, Ioannis Rekleitis, Milena Scaccia and Gregory Dudek

Optical flow , A tutorial of the paper:

Range Imaging Through Triangulation

CAP 5415 Computer Vision Fall 2012 Dr. Mubarak Shah Lecture-5

What I learned in the first 2 weeks

Coupled Horn-Schunck and Lukas-Kanade for image processing

Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION

Recovering observer motion

Find the derivative of the following function: {image} .

Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION

Presentation transcript:

1.Optical Flow 2.LogPolar Transform 3.Inertial Sensor 4.Corner Detection 5. Feature Tracking 6.Lines

1.Optical Flow The nearest common point of intersection with the horizon line is calculated

1.Optical Flow Parameters of the optical flow algorithm: Nonlinearity criterion tau_l Minimum number of valid component velocities N_min Parameters of the image sequence: Sequence itself Sampling rate Number of frames Performance values of the optical flow algorithm: Coverage of the optical flow field Error of the optical flow Performance values of the FOE algorithm: Coverage of the expansion field Error of the estimated FOE position

1.Optical Flow Parameters tau_l: N_min: 7 1st Frame: 1 Sampling Rate: 4 Time Span: 3 Performance Optical Flow Field Coverage: 5.39 % Expansion Field Coverage: 4.9 % Absolute error FOE: 1.76 Pixel FOE: , 51 The first try

1.Optical Flow Empirical behaviour along the image sequence Average Flow Vector Coverage: 4.5% Average Expansion Vector Coverage: 3.4% No calculation possible Average FOE Error: 13.6% tau_l = N_min = 7 samp = 4 st = 3

1.Optical Flow N_min = 7 samp = 4 st = 3 31 Samples With tau_l > 0.1 the Error does not change much

1.Optical Flow tau_l = 0.1 samp = 4 nof = 3 31 Samples A low number of component velocities gives the smallest error

1.Optical Flow tau_l = 0.1 N_min = 2 nof = 3 21 Samples

1.Optical Flow Open questions: Where lies a pure noise? Is a lens distortion visible?

Parameters gx: 25 tau_l: N_min: 1 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 100 % Vectors: 408 Rejection Rate: 13.0 % Parameters gx: 25 tau_l: N_min: 2 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 98 % Vectors: 399 Rejection Rate: 14.0 %

Parameters gx: 25 tau_l: N_min: 3 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 99 % Vectors: 405 Rejection Rate: 13.8 % Parameters gx: 25 tau_l: N_min: 4 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 98 % Vectors: 399 Rejected: 14.0 %

Parameters gx: 25 tau_l: N_min: 5 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 95 % Vectors: 386 Rejection Rate: 14.0 % Error along x: Pixel Parameters gx: 25 tau_l: N_min: 6 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 87 % Vectors: 356 Rejection Rate: 15.2 %

Parameters gx: 25 tau_l: N_min: 7 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 78 % Vectors: 320 Rejection Rate: 15.9 % Parameters gx: 25 tau_l: N_min: 8 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 69 % Vectors: 280 Rejection Rate: 17.1 %

Parameters gx: 25 tau_l: N_min: 9 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 53 % Vectors: 216 Rejection Rate: 19 % Parameters gx: 25 tau_l: N_min: 10 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 30 % Vectors: 121 Rejection Rate: 19.8

Parameters gx: 25 tau_l: N_min: 11 1st Frame: 44 Sampling Rate: 4 Time Span: 3 Performance Coverage: 11 % Vectors: 46 Rejection Rate: 13 %

6,9,12 5,9,13 Reasons for the Peak : Image 9 moves to the right The vectors with a strong right component are still supported