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I didn't read the paper but let me provide some intuition about object detection and tracking. When you try to track a target in a video, object detection algorithms might not be enough and you need to support the algorithm with target tracking methods in bayesian framework. It could be Kalman filter or particle filter. In particle filter or any Bayesian ...


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I implemented many types of particle filter for one sensor. Since your case(i.e sensor fusion) includes multiple sensors, I try to make analogy using KF which I used many times for sensor fusion. In KF you perform time update and measurement update for each upcoming measurement sequentially. And if your measurements are asyncronous you modify your time ...


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There is a strict difference between KF and ML. Former provides density of the state while the latter provides a point estimate. So, they are not alternative of each others exactly. But, sure you might use ML instead of KF if you need point estimate.


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