I agree with @Maurits on his comment
Assuming that you want to develop a simple system as a part of mini-project or something here is what you may do.
The basic concept is that the places where there is change (along time) is the places where there are objects. So what you need to do as 1st step is change detection. A simple algorithm to do change detection is frame-differencing. What is essentially done is to subtract consecutive frames. And the absolute value of this differencing is threshold. What you really will do with this is that if difference in value is high then it is a pixel where there is change. If difference is not too much then it is not a change.
At this stage you should get a binary image (containing 0s and 1s). This image shall contain several blobs. You can iterate through these blobs by using connect component labelling. Implementations of these are available with matlab and opencv.
After this you can have a simple rule like. If the ratio of width by height is more than 1 then it is a car else a human. This rule is applicable since cars are wide and humans are tall. (I hope you get what I am trying to say).
Now there could be a few noise like objects. These can be filtered out based on their area. For example if the blob size is less than a threshold you can ignore that blob.
I hope with this simple approach you can make a system which can detect a car and a human from fixed background video. Please be warned that this is a simple apprach and likely is not robust. But should work fine under a few more assumptions.