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Detect object, find state of object. Update state, use state to predict next state.
Build a motion model to track objects. Motion model can be learnt.
Hard to detect objects in scene. Particle tracker only helps in tracking not detection.
Kalman Filter.
Predict object location. Very Robust to noise.
Incremental approach. Computational solution to lest squares.
Process model = > Transition matrix, External control of state and noise.
xt = Axt-1 + But-1 + vt-1. x may be hidden and only z can be observed.
zt = Hxt + vt. Observation model .
Make prediction. yt = Axt-1 + But-1
Estimate error = Apt-1A' + Q
Find Kalman Gain
xt = yt + kt(zt - Hyt). Update position vectors based on Kalman gain.
Estimate new error. Et = (I - KtH)Et-1
Takeaways : Roboust to noise, generic model. Flixebility of state vectors.
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