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What are the challenges of Video Analytics?

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With the birth of video analytics, the manufacturing industry is on its way to a new face. The new combination of AI and manufacturing promises a better, more efficient and hassle-free operating environment in factories.
Challenges of video analytics

Source- McKinsey & Company


With the birth of video analytics, the manufacturing industry is on its way to a new face. The new combination of AI and manufacturing promises a better, more efficient and hassle-free operating environment in factories. With applications like video analytics, AI-powered manufacturing is set to change the way you work with machines in a factory, all with lesser hassles and better, more refined results.

But does this hassle-free technology really face no challenge? The answer is a simple no.

While industry 4.0 or the digitized industry has already proven its worth and is still very promising, there are some drawbacks that need to be worked on.

Some of the challenges that AI-powered manufacturing, especially video analytics, faces today include:

Cameras

The whole process of video analytics stands on two pillars - the recording system and the analysis system. The recording system is what we commonly call CCTVs or simply cameras. These cameras are the tools that capture the video(s) and feed it to the software. This software, in turn, helps the user make a decision based on the analysis.

But what if the video that has been fed to the system isn't good enough? Will the analysis be good enough? Will the final decision be good enough? Will there be any decision at all?

In order to avoid struggling with such questions, the first step would be to improve the video quality. This can only be done by improving the quality of the video-recorder, the camera being used. The quality of a video recorder can be assessed on the resolution of the video, whether it is UltraHD or SD; the speed of the video. The type of camera used depends solely on the type of use case.

Data Drift

Even though the cameras may be as per the requirements, what happens seldom is what AI engineers call as Data or Content Drift. This simply means frequent changes coming in the data, as compared to the training data. This happens gradually and causes dynamic AI algorithms to sometimes produced less accurate results.

The solution is to constantly monitor the models and keep them updated with continuous training and features. This is one of the primary reasons why SaaS works best for AI implementations in any industry.

Video Processing

Videos are different from the text in more aspects than one; their size being the biggest difference. Videos have large data volumes and have a rich variety of content. In order to record and further analyze videos, complex tools are required. These tools have both hardware and software components.

Simply put, the cameras for recording and the software for analyzing the recorded videos both have to be top-notch. As the lifecycle of such hardware and software tools gets shortened with every passing day, it is tough to strike a perfect balance between the two.

In addition to this, processing requires sophisticated technology that may not be easy to use for everyone. These softwares may also take a considerably long time to process things, which makes the time constraint another challenge for video analytics.

Real-Time Processing

One of the main reasons for using analytics with supervision is to aid in the real-time processing of videos. Real-time processing helps the system recognize people and objects in real-time to identify specific events like accidents and to send out alerts to the user. A person moving suspiciously, a part falling to the factory floor, smoke or flames in some part of the facility are some examples of what all can be observed in real-time using video analytics. This means, all of these issues, amongst many others, can be better dealt with and with some advancements can be avoided as well.

The inclusion of AI into every aspect of the operations requires a change in all the tactical and strategic decisions taken.

Data storage challenge

The amount of data collected via video-analytics software has grown five-fold between the years 2015 and 2019 and is expected to grow even more. With the tremendous amount of data collected, the storage of data is becoming a concern. Increased privacy norms are another factor to be taken into consideration. However, cloud services like AWS or Azure ensure smooth access and storage of your data.

Access and management challenge

The data collected by CCTV surveillance systems are only as good as it can be managed by your team. If the data isn't managed properly by the human resources you have deployed to do so, the data would be as good as one that was never collected, meaning the data remains useless and your resources employed remain so too. It is imperative to train your employees in order to best utilize the software. Services like Hadoop play a key role in managing data from multiple sources.

Security challenge

With increasing cases of hacking and internet leaks reported every day across the globe, the security aspect of the CCTV supervision system poses a big question for the daily operations of your business. In order to keep the supervision system working for you instead of against you, it is important to observe measures like regular, comprehensive updates, strong hardware-based firewalls, and complete data backups. Also, these systems must be updated regularly.

As all the data is integrated, the risks that come with online attacks and losing the data also increase manifold.

Social impact

Video analytics, like every other AI-powered solution, comes with a lot of supposed threats to incumbents. But that's all these threats are, 'supposed'. While video analytics eliminates the need for a person to roam around in the factory examining the production line, it does not replace them. Video analytics essentially acts as the eyes and ears of the person but not their mind. That is, the person doing the job using video analytics plays just as important a role as they did before using video analytics. To make things simpler, imagine this: Video analytics is what provides the information on which to base the decision while the user is actually the decision-taker.

The focus today is on the adoption of new technology in a way that does not result in hundreds of people losing their jobs overnight.

With all of the above-mentioned challenges, it becomes a tough task for users to choose a technology that might best suit their needs and at the same time promise good results. But considering the pace at which improvements in technology take place these days, it is only obvious that soon these challenges would be taken care of as well.

To conclude, we can say that if video analytics provides 7 benefits, it also has a few challenges that need to be taken care of in order to improve its reliability.

With the birth of video analytics, the manufacturing industry is on its way to a new face. The new combination of AI and manufacturing promises a better, more efficient and hassle-free operating environment in factories. With applications like video analytics, AI-powered manufacturing is set to change the way you work with machines in a factory, all with lesser hassles and better, more refined results.

Click here to know more about Video Analytics and how it can increase your company's productivity.