Tracking
Describes the fundamental mathematics of tracking with explanation of Kalman Filter and Particle Filter
Model for Tracking
State
Observation
To understand this fully, we can draw parallel to control systems concept of state-space model:
Also, the above two equations are called transition model and observation model in our tracking literature. As they denotes, how our model is gonna transition and what is the relation between bservation and state.
Tracking: Prediction and Filtering
Kalman Filter
Particle Filter
From the course course on Computer Vision
The Summary Video for 7B-L1 is very good for overall concept.
Kalman Filter:-
Extended Kalman Filter is used for NON-LINEAR Models.
Particle Filter
Watch this for basic idea :- https://youtu.be/aUkBa1zMKv4
Note that they assume that MAP OF ENVIRONMENT IS KNOWN.
7C - L2 - Very good for understanding robot motion using particle filters.
9th video 7C-L3 particle filter for practical consideration is important.
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