Case Overview

Video Analytics or more precisely, Video Content Analytics haven’t quite matched the hype. Beside the well-founded concerns of privacy and security, there are functional and non-functional challenges to a successful video analytics solution deployment. 

The promise of machine learning and artificial intelligence driven video analytics only if all issues and concerns are addressed – and knowing them is the first step towards finding solutions to those concerns. We discuss some of them in this post.

a. False Alarms

Any machine learning or artificial intelligence based algorithm has an inherent error – commonly classified as Type I and Type II errors.

Type I Error: An event of interest has not occurred but the algorithm think it has

Type II Error: An event of interest has occurred but the algorithm fails to detect it

These errors create nuisance and a general frustration for the agency where the solution is being tested or deployed. A feeling of half baked solution with no understanding of when to react to an alarm and when to let it go may seep-in in the longer term

b. Impact of ambient factors

The performance of a video analytics solution is highly dependent on feed quality. If there are extreme weather events – heavy rains, sudden dark clouds, gathering of large number of people, a traffic jam etc. – the performance of the video analytics solution may falter. And as these conditions are some of the extreme cases for which video analytics solution has been sought – the trust may completely evaporate

c. Unsuitable use cases

This problem occurs because of the hype and mis-selling associated with machine learning and artificial intelligence based video analytics solutions. While not a problem with the solution itself, but unachievable promises by the selling party may lead to disillusionment and negative publicity for the solutions

d. Machine vs. Human Interpretation

Video Analytics solutions are based on machine learning & artificial intelligence algorithms. Machines process and understand a feed differently than humans do. Infact, unless explicitly programmed, these algorithms cannot interact with (or read emotional response of) the users to find out if it’s response to a query was useful.

e. Cost

The cost of an effective and intelligent video analytics solution suffer with huge upfront deployment cost and high maintenance cost. The infrastructure required to process all the feed generated through cameras is not cheap – despite all the progress made on cheaper bandwidth and databases

f. Privacy concerns

More than 80% of world population doesn’t live in China. Citizens have a say on their privacy rights and can punish governments if they feel the State is snooping on them at will. And being a responsible company, we should point out here that these concerns are not unjustified. There is a high risk of the solution being misused. And our hypothesis is, if something can be misused – it will be. It is therefore important for the agency to look carefully into the possible misuses – and create ironclad checks and balances

Video analytics can be a gamechanger – if implemented to bring effective change. While we have not found any study establishing the benefits of video analytics solution bring about safer societies e.g. Reduction in crime rates on implementation of a city surveillance system, we strongly believe in the potential of video analytics.

To be successful, the requirements should be well defined and apart from specific challenges, the concerns raised above should be given a careful consideration.

Xtage Labs is an advanced analytics and machine learning based decision insights company. We work with businesses to derive insights from data, and improve the decision making processes.

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