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Online Multi-camera Tracking
with a Switiching State-Space
Model
Wojciech Zajdel, A. Taylan Cemgil,
and Ben KrÄose
ICPR 2004
Networks of Non-Overlapping
Cameras
Yk={Ok, Dk}
….
Yk={Ok, Dk}
….
Yk={Ok, Dk}
….
Yk={Ok, Dk}
….
Yk={Ok, Dk}
….
Ok = Description of observation (colour vector in this case) – (assumed noisy)
Dk = Camera no., time (assumed non-noisy)
Appearance is a “Noisy
Observation”
• Assume observed appearance is a random
sample from some distribution of possible
(probable) appearances of an object.
• Represent this as a latent variable with mean
and covariance;
Xk={mk,Vk}
• We have a prior (X) over the parameters of this
model (a “Normal-Inverse Wishart distribution”)
Appearance Model
Tracking is Just Association
• Tracking is just associating our Ds (camera,time) with a
particular object i.e.
{D1(n), D2(n),D3(n), …}
Defines a sequence of observations of “person n” over
time.
Also represent this information (redundantly) as;
Sk i.e. the label of person to which observation Yk is
assigned
N.B. For K observations there is a maximum of K
possible people! (i.e. we don’t know the people, but
define potential people by each new observation)
But, how many actual people are
there??
• We’ve said before the maximum for a
sequence of K observations is K people.
• Ck is the actual number of trajectories
(people); Ck<=K
• Related concept: Zk = index to last time
person k was observed (can be NULL if
first time person was observed)
Camera Network Topology
• Topology defines valid (or likely) paths through
the network .. Defined (in a Markov like way) as:
P(Di+1(n) |Di(n))
• i.e. the probability that observation Di+1 results
from object n, given observation Di does.
• In this paper is uniform over possible paths (and
0 for impossible paths) .. But others have done
more complex things.
A Predictive Model
• Rather than search through the space of
possible associations and optimise some fitness
measure, it is sometimes easier to define a
predictive model and work backwards from the
observations to estimate probabilities over the
association variables;
Association
Variables
Appearance
Distributions
Observations
Hk = {Sk, Ck, Zk(1),….. Zk(k)} … i.e. the association variables
Tracking as Filtering
• Once we have a predictive model we can “filter” data on
a predict;observe;update cycle.
• This is (in some sense) an alternative to searching
through possible latent variable values to maximise the
posterior probability (e.g. MCMC as introduced by
Krishna)…
• Only usually tractable under simplifying conditions, e.g.
Kalman filter; Gaussian probabilities
Particle Filter; Probabilities represented as a finite
number of samples
Predictive Model: Predict Step
Predictive density (i.e. without considering Yk, the latest observation):
Current associations given
previous; defined a-priori
from topology
Joint probability of
latent variables
(i.e. the unknowns)*
(*possibly should be
conditioned on past
observations?)
Current appearance given
previous appearance and
associations; defined based
on appearance of a person
not changing and sampling
new people from a prior
From the previous
iteration
(N.B. t0 is easy as there
are no people)
Filtered density
Normalising factor
Probability of latent
variables, given the
current observation
Prediction (from previous
slide) i.e. probability of
latent variables
Probability of observation
given latent parameters
(i.e. associations +
appearances)
N.B. Latent variables H are discrete, whereas the variables X are continuous
BUT: Result is a mixture of O(k!) density functions => intractable 
How to filter??
• If all latent variables were discrete (which
they are not) we could maintain
probabilities for all combinations of latent
variable values (but this might be a lot!)
• We could use something like a particle
filter to approximate the densities (others
have done this, but this is not what these
guys have done)
Their Solution
• Reformulate the filtered density using an
approximation that is more tractable
Labels and count Appearance
at current step
(continuous .. But assume
(discrete)
“simple” distribution)
i.e. rather than maintaining a distribution over all of H (the
possible associations .. Quite a big set potentially) a set of
simpler distributions are maintained over S/C/Z at the current
step (remember S= label of Xk, C=no. of trajectories, Z=last
instance time).
The product of these simpler distributions approximates the true
filtered density
Can go back to the original problem (i.e. estimating the complete
H) by finding the product of marginals (more later!)
Time of last
observation of k
(discrete)
Their Solution – Presented
Differently (Technical Report
Version)
ftp://ftp.wins.uva.nl/pub/computer-systems/aut-sys/reports/IAS-UVA-04-03.pdf
(same thing, slightly different notation)
NB. Appearance conditioned
on theta (same form as
parameters of the prior on
appearance ..
An “Inverse Wishart density” )
An Aside: Marginals and Product of
Marginals
• Imagine a joint density over 2 variables x and y p(x,y)
n*m bins
P(X,Y)
X
Y
• If variables x and y are (reasonably) independent, then
we can “marginalise” over one of the variables (or the
other) by summing over all values.
P(X)
P(Y)
X
Y
=> We’ve removed the dependency & work with them separately…
n+m bins
Marginals and Product of Marginals
• We can then go back to the original
representation by taking the product for
each pair of values of x and y:
• P(x,y) = p(x)*p(y)
Results
• Method compared to;
i) MCMC (similar idea to Krishnas
presentation last week)
ii) Multiple Hypothesis Tracking (i.e. a
hypothesis pruning based method)
• It does better (others over-estimate no. of
trajectories)
Drawbacks .. And solution
• K grows with number of observations and
memory usage O(k2) .. Although
complexity is only O(k) [I think]
• Pruning is used to keep this down
(removing the least likely to be a trajectory
end point)
Summary
There is more than one way to skin a cat;
• ADF (this paper) – Approximating the
problem, solving exactly
• MCMC – Exact problem, but
approximating the solution (stochastic)
• MHT - Exact problem, but approximating
the solution (via hypothesis pruning)