a particle filter to track multiple objects carine hue patrick pérez and jean-pierre le cadre
TRANSCRIPT
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A particle filter to track multiple objects
Carine Hue
Patrick Pérez and Jean-Pierre Le Cadre
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Context Two applications:
Signal processing: target tracking
Image analysis: people tracking
observer
target
bearing
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Challenges Generic tracking issues
(re)initialization Clutter (false alarms or background) Occlusions
Specific multiple-object issues Complexity Identity confusion when crossing Varying number of entities
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Basic ingredients
Hidden variables (position, velocity, shape of the objects)
Data ( bearings, images at each time)
Estimation of posterior
Dynamics:
Likelihood:
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Hidden variables Low-dimensional single-object hidden variable:
Bearings-only tracking:
People tracking:
: Fourier descriptors
: 2D translation vector of the center
Multiple-object hidden variable
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Single-object dynamics prior: first-order auto-regressive process
Multi-object dynamics: independent single-object dynamics
Dynamics
time t-1
time t ??
velocity vector
position orfirst Fourier coef.
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Bearings-only tracking: highly non-linear wrt the state variable
People tracking: obtained with a motion-based segmentation
How to assign the data to the states ?
Data
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Data association Notation: association vector: if is issued from object Association assumptions:
one measurement can originate from one object or from the clutter (false alarms)
one object can produce zero or several measurements at one time Exhaustive enumeration ! NP-hard problem
Solution: probabilistic association:
false alarms
Irisa:Irisa:
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Particle filtering
Current cloud at time t
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Particle filtering
Propagation by sampling from the dynamic prioror from an importance function based on the data
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Particle filtering
Weighting according to data likelihood
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Particle filtering
Resampling from the weighted particle set
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Particle filtering
Prediction again . . .
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Example: bearings-only tracking Particle cloud for one object
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Multi-object data likelihood
Estimation of the association probabilities vectorwith a Gibbs sampler
take the average value of
t
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Example: bearings-only tracking Measurement equation
Differences between the simulated bearings for each object
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Example: bearings-only tracking Estimated association probabilities
Estimated trajectories
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Example: people tracking Data likelihood
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Example: people tracking
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Conclusion and further work Generic multi-object tracker based on a mix of particle
filteringand Gibbs sampling
suitable for various applications
Drawbacks Costly Fixed number of objects For people tracking, the data association could be avoidedwhen there is no ambiguities
Perspectives Varying number of objects Initialization issue Test with real bearings measurements
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The End
Questions?