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# lec20 - Announcements Visual Tracking HW4 due tomorrow at...

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1 CS252A, Fall 2010 Computer Vision I Visual Tracking Computer Vision I CSE252A Lecture 19 CS252A, Fall 2010 Computer Vision I Announcements • HW4 due tomorrow at midnight • Final exam: Wed (12/8) 11:30-2:30 in WLH 2204 CS252A, Fall 2010 Computer Vision I Main tracking notions • State: usually a finite number of parameters (a vector) that characterizes the “state” (e.g., location, size, pose, deformation of thing being tracked. • Dynamics: How does the state change over time? How is that changed constrained? • Representation: How do you represent the thing being tracked • Prediction: Given the state at time t-1, what is an estimate of the state at time t ? • Correction: Given the predicted state at time t , and a measurement at time t , update the state. • Initialization – what is the state at time t=0? CS252A, Fall 2010 Computer Vision I What is state? 2-D image location, Φ =(u,v) Image location + scale Φ =(u,v,s) Image location + scale + orientation Φ =(u,v,s, θ ) Affine transformation 3-D pose 3-D pose plus internal shape parameters (some may be discrete). – e.g., for a face, 3-D pose +facial expression using FACS + eye state (open/closed). Collections of control points specifying a spline Above, but for multiple objects (e.g. tracking a formation of airplanes). Augment above with temporal derivatives CS252A, Fall 2010 Computer Vision I Example: Blob Tracker From input image I(u,v) (color?) at time t , create a binary image by applying a function f(I(u,v)). Clean up binary image using morphological operators Perform connected component exploration to find “blobs.” – connected regions. Compute their moments (mean and covariance of coordinates of region), and use as state Using state estimate from t-1 and perform “data association” to identify state in from t. CS252A, Fall 2010 Computer Vision I Tracking: Probabilistic framework • Very general model: – We assume there are moving objects, which have an underlying state X – There are measurements Y, some of which are functions of this state – There is a clock • at each tick, the state changes • at each tick, we get a new observation

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2 CS252A, Fall 2010 Computer Vision I Tracking State Instead of “knowing state” at each instant, we treat the state as random variables X t characterized by a pdf P(X t ) or perhaps conditioned on other Random Variables e.g., P(X t | X t-1 ), etc. The observation (measurement) Y t is a random variable conditioned on the state P(Y t | X t ) Generally, we don’t observe the state – it’s hidden. X 0 X 1 X t-1 X t X t+1 Y 0 Y 1 Y t-1 Y t Y t+1 CS252A, Fall 2010 Computer Vision I Three main steps We can try to express these conditional distributions parametrically, sample the distribution, or estimate the mode. CS252A, Fall 2010
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## This note was uploaded on 12/08/2010 for the course CSE 252a taught by Professor Staff during the Fall '08 term at UCSD.

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lec20 - Announcements Visual Tracking HW4 due tomorrow at...

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