Video Analytics (AI)

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Like Video Recording, it’s assumed that Video Analytics are all the same. They are not.

There are 3 major components:

The base technology being used:

  • VMD – Video Motion Detection (Moving Pixel Blobs – prone to many false alarms)

  • VMD + Heuristics (prone to misclassifications of objects)

  • VMD + Machine Learning (More accurate but computationally difficult, if the subject changes in anyway, accuracy is reduced e.g. someone wears a mask)

  • Deep Learning – Is a subset of Machine Learning. It’s fast and highly accurate, object gets recognised in different positions/forms, BUT requires a lot of computational power, accuracy is greatly dependent on dataset quality (diversity and number of images learnt)

The processing power of the hardware the analytic is being run on the ‘edge’ (e.g., camera with limited processing power) or on a server which typically has more processing power to process video data.

AI Business Management Toolbox

Here is what we have chosen for our Tech Stack:

  • We’ve taken ‘Deep Learning’ technology with expansive data sets (to improve accuracy) and written filters and conditions around it so that an integrator can script a custom solution to suit a customers environment

  • A wide variety of objects that have been ‘Deep Learnt’

  • We can mix and match AI Metadata from edge devices and internal or external servers to deliver the best outcome

  • Server based AI is very high performance – and means you can use almost any existing camera

  • We’ve optimized hardware/software to run BOTH VMS and Deep Learning Servers on the one machine. This saves $1000’s

Example of Deep learning analytics