Download presentation

Presentation is loading. Please wait.

Published byJordan Fagan Modified over 3 years ago

1
SAMSI Discussion Session Random Sets/ Point Processes in Multi-Object Tracking: Vo Dr Daniel Clark EECE Department Heriot-Watt University UK

2
observation produced by targets target motion state space observation space 5 targets 3 targets X k-1 XkXk Number of states and their values are (random) variables Need to estimate the number of target states and their state vectors online Multi-object filtering with point processes Multi-object filtering with point processes

3
state space vk vk v k-1 PHD filter v k-1 (x k-1 |Z 1:k-1 )v k (x k |Z 1:k ) v k|k-1 (x k |Z 1:k-1 ) PHD prediction PHD update Multi-object Bayes filter p k-1 (X k-1 |Z 1:k-1 ) p k (X k |Z 1:k ) p k|k-1 (X k |Z 1:k-1 ) prediction update PHD filters PHD filters

4
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 Approximation Strategies

5
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: Problems:

6
Complexity: -How does the complexity/ reliability of the approach scale with the number of targets? -Poisson PP: mean=var Problems: Problems:

7
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: Problems:

8
How do we obtain tracks/ trajectories of individual targets? - Possible solutions – include track id in the state / find greatest intersection of particles Problems: Problems:

Similar presentations

OK

Inferring High-Level Behavior from Low-Level Sensors Donald J. Patterson, Lin Liao, Dieter Fox, and Henry Kautz.

Inferring High-Level Behavior from Low-Level Sensors Donald J. Patterson, Lin Liao, Dieter Fox, and Henry Kautz.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on cross docking meaning Download ppt on subject and predicate Ppt on online shopping project Ppt on acute coronary syndrome symptoms Ppt on non ferrous metals Ppt on information security Ppt on the road not taken analysis Ppt on production planning Ppt on job evaluation process Ppt on endangered species in india