<p>Target tracking is a very difficult problem. In target tracking you will have <strong>two main issues</strong>: the motion uncertainty problem, and the origin uncertainty problem. The first one refers to the way you model object motion so you can predict its future state, and the second refers to the issue of data association(what measurement corresponds to what track, and the literature is filled with <a href="https://www.mdpi.com/1424-8220/20/4/1110" rel="nofollow noreferrer">scientific</a> ways in which this issue can be approached).</p>

<p>Before you can come up with a solution to your problem you will have to answer some questions yourself, regarding the tracking problem you want to solve. For example: what are the values that you what to track(this will define your state vector), how are those values related to one another, are you trying to perform single object tracking or multiple object tracking, how are the objects moving( do they have a relatively constant acceleration or velocity ) or not, do objects make turns, can objects also be occluded or not and so on.</p>

<p>The <strong>Kalman Filter</strong> is good solution to predict the next state of your system (once you have identified your process model). A deep learning alternative to the Kalman filter is the so called <a href="https://arxiv.org/abs/1511.05121" rel="nofollow noreferrer">Deep Kalman Filter</a> which essentially is used to do the same thing. In case your process or measurement models are not linear, you will have to linearize them before predicting the next state. Some solutions that deal with non-linear process or measurement models are the <strong>Extended Kalman Filter</strong> (EKF) or <strong><a href="https://towardsdatascience.com/the-unscented-kalman-filter-anything-ekf-can-do-i-can-do-it-better-ce7c773cf88d" rel="nofollow noreferrer">Unscented Kalman Filter</a></strong> (UKF). </p>

<p>Now related to fast moving objects, an idea you can use is to have a larger covariance matrix since the objects can move a lot more if they are fast, so the search space for the correct association has to be a bit larger. Additionally you can use multiple motion models in case your motion model cannot be satisfied with only one model. 
In case of occlusions I will leave you <a href="https://stackoverflow.com/questions/2764238/image-processing-what-are-occlusions/60644446#60644446">this</a> stack overflow thread, where I have given an answer covering more details regarding occlusion handling in case of tracking. I have added some references for you to read. You will have to provide more details in your question, if you would like to receive more information regarding a solution (for example you should define fast moving objects with respect to camera frame rate). </p>

<p>I personally do not think there is a silver bullet solution for the tracking problem, I prefer to tailor a solution to the problem I am trying to solve. </p>
How to track Fast Moving Objects?
<p>I'm trying to create an application that will be able to track rapidly moving objects in video/camera feed, however have not found any CV/DL solution that is good enough. Can you recommend any computer vision solution for tracking fast moving objects on regular laptop computer and web cam? A demo app would be ideal.</p>

<p>For example see this video where the tracking is done in hardware (I'm looking for software solution) : <a href="https://www.youtube.com/watch?v=qn5YQVvW-hQ" rel="nofollow noreferrer">https://www.youtube.com/watch?v=qn5YQVvW-hQ</a></p>