Signal Processing for Discrete Events
To do something about the "Mother with kids"-Problem, you could implement an algorithm that sets a value to 1 or 0 based on whether the sensor was activated in a timeframe of ~5 seconds. Saving this value every 5 seconds leads to equidistant samples in time domain.

The coming in/going out-problem could be solved using two IR sensors that customers have to pass one after another to enter or leave. By checking whether the activation time difference is positive or negative, you can find out if they were coming or leaving.

I would not recommend interpolation as customer appearance is not necessary bound to any pattern or formula but somewhat random since it depends on a lot of things not easy to predict.
In this case, consider using histograms for fixed time intervals (15/30/60 minutes) to visualize customer spread (rush hours ...) .

Assuming you take aforementioned samples every 5 seconds, you are working with value- and time discrete signals which may or may not be deterministic/periodic (predictable) but will be random/stochastic to some extend.