A particle filter to track multiple objects

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

Context • Two applications: • Signal processing: target tracking • Image analysis: people tracking target bearing observer

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

Basic ingredients • Hidden variables (position, velocity, shape of the objects) • Data ( bearings, images at each time) • Estimation of posterior • Dynamics: • Likelihood:

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

Dynamics • Single-object dynamics prior: first-order auto-regressive process • Multi-object dynamics: independent single-object dynamics velocity vector position or first Fourier coef. time t-1 time t ??

Data • 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 ?

Irisa: 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

Particle filtering Current cloud at time t

Particle filtering Propagation by sampling from the dynamic prior or from an importance function based on the data

Particle filtering Weighting according to data likelihood

Particle filtering Resampling from the weighted particle set

Particle filtering Prediction again . . .

Example: bearings-only tracking Particle cloud for one object

Multi-object data likelihood • Estimation of the association probabilities vector with a Gibbs sampler take the average value of

Example: bearings-only tracking • Measurement equation • Differences between the simulated bearings for each object

Example: bearings-only tracking • Estimated association probabilities • Estimated trajectories

Example: people tracking • Data likelihood

Example: people tracking

Conclusion and further work • Generic multi-object tracker based on a mix of particle filtering and Gibbs sampling • suitable for various applications • Drawbacks • Costly • Fixed number of objects • For people tracking, the data association could be avoided when there is no ambiguities • Perspectives • Varying number of objects • Initialization issue • Test with real bearings measurements

TheEnd Questions?

A particle filter to track multiple objects