We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to ...
The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. ...
— Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switc...
This paper presents a novel approach of multiple target tracking from multiple collaborative cameras. Firstly, particle filtering for conditional density propagation on graphs to...