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SAMSI Discussion Session Random Sets/ Point Processes in Multi-Object Tracking: Vo. Dr Daniel Clark EECE Department Heriot-Watt University UK. Multi-object filtering with point processes. observation space. observation produced by targets. state space. target motion. X k. X k- 1.
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SAMSI Discussion SessionRandom Sets/ Point Processes in Multi-Object Tracking: Vo Dr Daniel ClarkEECE DepartmentHeriot-Watt University UK
Multi-object filtering with point processes observation space observationproduced by targets state space target motion Xk Xk-1 3 targets 5 targets Number of states and their values are (random) variables Need to estimate the number of target states and their state vectors online
PHD filters state space vk vk-1 PHD filter PHD prediction PHD update vk|k-1(xk|Z1:k-1) vk-1(xk-1|Z1:k-1) vk(xk|Z1:k) prediction pk-1(Xk-1|Z1:k-1) pk|k-1(Xk|Z1:k-1) pk(Xk|Z1:k) update Multi-object Bayes filter
PHD: assumes that the prior intensity is Poisson MeMBer: assumes multi-Bernoulli i.e. each target is assumed to be Bernoulli with probability of target existence PHD/CPHD filters propagate an intensity function of a point process Approximation Strategies
Problems: How do we estimate single/ multiple target states from a multi-modal particle density? - Clustering algorithms such as k-means and EM can be unreliable
Problems: Complexity: • How does the complexity/ reliability of the approach scale with the number of targets? • Poisson PP: mean=var
Problems: SMC implementations for filtering propagate intensity functions not probability densities • Usual convergence properties of SMC algorithms of probability distributions needs modifying. • Non Feynman-Kac model.
Problems: How do we obtain tracks/ trajectories of individual targets? - Possible solutions – include track id in the state / find greatest intersection of particles