Situations where complexity is too big to exceed linearity and gaussianity
Well, let's look at the two issues: 1) linearity and 2) Gaussianity.

Linearity

If you're imaging moving 3D objects (people) with a single camera, then you're working with a 2D projection of those 3D objects.  That dimensionality reduction can cause non-linearities to appear.

Take a 2D to 1D example:  an object moving in a circle in 2D.  The object is moving with constant velocity and constant acceleration.  All nice and linear.  Project that object onto a 1D surface (a line) and the velocity and acceleration are now (co)sinusoidal.

One of the underlying assumptions in Kalman filtering (well, the sort usually presented) is that the system you're modeling is linear.  When dimensionality reduction (3D $\rightarrow$ 2D) is involved, this may not be true.

Gaussianity

When a system is linear, any noises tend to be additive.  When Gaussian noise is added to Gaussian noise (or, for enough samples, noise of just about any distribution is added to noise of just about any distribution), the result is also Gaussian.

If the system is non-linear, then the operations involved cause Gaussian noise to be transformed into potentially non-Gaussian noise.  

For example, if you divide one Gaussian variable with another, the distribution of the result is Cauchy.

What's so bad about that?  The mean (average) of a Cauchy random variable does not exist.  The variance of a Cauchy random variable does not exist.

The Kalman filter is all about tracking means and variances of variables in the system.  If the system is non-linear, it's possible that the relevant quantities' means and variances do not exist, so application of a Kalman filter is meaningless.