Learning to Track Motion - · Learning to Track Motion Maitreyi Nanjanath Amit Bose CSci 8980...
Transcript of Learning to Track Motion - · Learning to Track Motion Maitreyi Nanjanath Amit Bose CSci 8980...
Learning to Track Motion
Maitreyi Nanjanath
Amit Bose
CSci 8980 Course Project
April 25, 2006
Background
• Vision sensor can provide a great deal of
information in a short sequence of images
• Useful for determining camera position
and movement
• Availability of local trackers:
– Fast
– Error prone
– Globally inconsistent
Motivation
• Global consistency as a cue for error correction
• Is a master algorithm possible over and above local trackers that makes corrections?
• Assume local tracking algorithm is a black-box– Input: sequence of images
– Output: image co-ordinates of successive feature positions
Problem Statement
• Given: Inaccurate estimates from a set of
local trackers
• Objective: Devise a master algorithm for
global tracking that,
– is based on erroneous estimates of local
trackers only
– can make relatively accurate estimates of
feature positions
Our Approach
• Hypothesis: It is possible to develop a
learning algorithm that performs
corrective tracking
• Problem naturally fits the multi-
dimensional regression model
– We have seen the generalized additive (GA)
algorithm of Kivinen and Warmuth
– We have also seen a multitude of loss
functions
Model
• N local trackers
• Each tracker generates 6 values– 2 spatial co-ordinates
– 2 velocity components
– 2 acceleration components
• Data-point (x, y)
– x: 6N+1 vector of tracker given information
– y: 2N vector of true spatial co-ordinates
Generalized Additive AlgorithmGeneralized Additive AlgorithmGeneralized Additive AlgorithmGeneralized Additive Algorithm
1. Initialize the parameter matrix as Θ1 = Θ.
2. Repeat for t = 1,…,ℓ
a) Get the input xt
b) Compute the weight matrix Ωt = Ψ(Θt)
c) Compute the linear activation ât = Ωt xt
d) Output the prediction ŷt = φ(ât).
3. For j = 1,…,k, update the jth row of the
parameter matrix by
θt+1,j = θt,j – η(ŷt,j – yt,j)xt
Experiment Setup
• A set of 20 points is generated– uniformly distributed over the unit cube
• The set of points is moved along a trajectory
• The path is projected onto 2 dimensions
• 6 values are generated for each point– Co-ordinates, velocity and acceleration components
– This forms the ground truth
• Ground truth is perturbed by adding noise
• Ground truth and noisy data is input to the GA Algorithm– It learns to produce corrected spatial coordinates
Noise added
• Gaussian noise, with Mean 0
– Variance 0.0025
– Variance 0.025
• Chi-squared noise, with Mean 0.1
• Arbitrary noise
– In this, certain data points suddenly become
zero. (which corresponds to losing a feature)
Transfer and Loss Functions
(∇Φ)-1(x) = logx
(∇Φ)-1(x) = – 1/x
(∇Φ)-1(x) = ex / (1 + ex )
(∇Φ)-1(x) = ex – 1
(∇Φ)-1(x) = x
Transfer Function
Φ(x) = ex“Exponential
loss”
Φ(x) = – logxItakura-Saito
Distance
Φ(x) = xlogx +
(1-x)log(1-x)Logistic loss
Φ(x) = xlogxI-Divergence
Φ(x) = x2/2Squared loss
Matching Loss Function
Training data
• 800 training data points:
– Correspond to set of image points moving
randomly
• Data scaled and shifted
– To fit constraints of loss functions.
• Training performed for many epochs: 1,
100, 300, 500
– Data shuffled randomly in every epoch
• Learning rate was kept fixed in a run
Results
• Test data:
– 400 data
points
• Results with
final weight
matrix
– generated for
both training
and test data
Results: Gaussian Noise and Squared Loss
Results: Gaussian Noise and I-Divergence
Results: Gaussian Noise and Logistic Loss
Results: Gaussian Noise and Itakura-Saito Distance
Results: Gaussian Noise and “Exponential” Loss
Results: Other Data Sets (Chi-square and arbitrary)
Issues and Challenges
• High learning rate made weights oscillate
• Data preparation was a major challenge:– Input had to be tuned for the loss functions
for specialized domains
– Appropriate noise additives were needed
– Choice of feature dimensions• Acceleration input was added to help track
rotation
• We were unsure if the simple regression model was sufficient
Extensions and Future Work
• Extensions:
– Varying learning rate
– Use non-linear high-dimensional mapping
and kernels
• Future work
– Regularization
– Using a structured regression model
– Testing on real images
Conclusions
• Over 75% reduction in error was seen in
most cases
• I-divergence and squared loss performed
comparably
• Itakura-Saito distance was not a good
choice for this domain
• Better tuning of parameters may improve
results
Q & A