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A particle filter to track multiple objects Carine Hue Patrick Pérez and Jean-Pierre Le Cadre

A particle filter to track multiple objects Carine Hue Patrick Pérez and Jean-Pierre Le Cadre.

Jan 04, 2016



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  • A particle filter to track multiple objects

    Carine Hue Patrick Prez and Jean-Pierre Le Cadre

  • ContextTwo applications:

    Signal processing: target tracking

    Image analysis: people tracking


  • ChallengesGeneric tracking issues(re)initialization Clutter (false alarms or background)Occlusions

    Specific multiple-object issuesComplexityIdentity confusion when crossingVarying number of entities

  • Basic ingredients

    Hidden variables (position, velocity, shape of the objects)

    Data ( bearings, images at each time)

    Estimation of posterior



  • Hidden variablesLow-dimensional single-object hidden variable:

    Bearings-only tracking:

    People tracking:

    : Fourier descriptors

    : 2D translation vector of the center

    Multiple-object hidden variable

  • Dynamics

    Single-object dynamics prior: first-order auto-regressive process

    Multi-object dynamics: independent single-object dynamics

    time t-1time t ??velocity vectorposition orfirst Fourier coef.

  • DataBearings-only tracking: highly non-linear wrt the state variablePeople tracking: obtained with a motion-based segmentation

    How to assign the data to the states ?

  • Data associationNotation: association vector: if is issued from objectAssociation assumptions:one measurement can originate from one object or from the clutter (false alarms)one object can produce zero or several measurements at one timeExhaustive enumeration ! NP-hard problem

    Solution: probabilistic association: false alarmsIrisa:

  • Particle filteringCurrent cloud at time t

  • Particle filteringPropagation by sampling from the dynamic prioror from an importance function based on the data

  • Particle filteringWeighting according to data likelihood

  • Particle filteringResampling from the weighted particle set

  • Particle filteringPrediction again . . .

  • Example: bearings-only tracking Particle cloud for one object

  • Multi-object data likelihood

    Estimation of the association probabilities vectorwith a Gibbs sampler

    take the average value of

  • Example: bearings-only trackingMeasurement equation

    Differences between the simulated bearings for each object

  • Example: bearings-only trackingEstimated association probabilities

    Estimated trajectories

  • Example: people trackingData likelihood

  • Example: people tracking

  • Conclusion and further workGeneric multi-object tracker based on a mix of particle filteringand Gibbs sampling suitable for various applications

    DrawbacksCostlyFixed number of objectsFor people tracking, the data association could be avoidedwhen there is no ambiguities

    PerspectivesVarying number of objectsInitialization issueTest with real bearings measurements

  • The End


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