ElectrifAi
July 8, 2021

How to Use Applied Ai in Video Analytics

Are you one of the many who use video for surveillance, security, predictive maintenance, etc.? If you do, then we have very intriguing ways to optimize and expand upon how you currently use video. And if not, you’ll want to start!

What do you do with your video? Does it sit stagnant in storage waiting for a chance to become valuable? Do you use it daily for surveillance purposes?

If your video sits in storage, untouched, why not have it actively working in the background by analyzing processes and give you feedback on how to improve? For example, Applied Ai with Computer Vision to can detect how well things are going through factory production lines and communicate to the team on how to improve.

If you use video for surveillance, wouldn’t you like to be able to respond to threats immediately, often before it even becomes apparent as a threat?

There are many video sources can be used in Applied Ai with video analytics. Here are a few examples:

  • Mobile Devices
  • Video Surveillance
  • Robotics
  • TVs and Movies
  • Games and Live Streams
  • Automatically detect specific objects in the video
  • Example: Know if the object is human and if they’re wearing the right safety gear. Ai can also determine who the person is so you can receive an alert, allowing you to take immediate action.

With so many use cases for Applied Ai to run video analytics, you can get information to optimize processes at your company. Wouldn’t you like to cut costs, increase revenue, and reduce risk?

What is Applied Ai?

Applied Artificial Intelligence (Ai) is the part of Ai for normal people in the real world who want to use Ai to complete real tasks.

What is Computer Vision?

Computers can “see” images and video and identify objects with a high degree of accuracy.

What is Video Analytics?

Technology that processes video footage using special machine learning algorithms to analyze the footage for specific purposes.

What can you do with video?

Automatic detection of objects

Identify properties of those objects

Recognize people, actions, and events

  • Example: Recognize specific faces to know if they’re supposed to be there at a certain time or if they shouldn’t arrive until 4 hours later. Also, know if someone who has been banned arrives.
  • Example: Know what’s going on in public places, such as a large group gathering and if support will be needed.

Semantic Analysis

  • Surveillance and robotics using activity detection
  • Example: Increase the effectiveness of a surveillance team, enabling them to identify and react much faster to threats.
  • Example: Robots used in distribution centers can choose which way to go depending on where other robots are on the floor.

Applied Ai Video Analytic Use Cases

Surveillance

Computer Vision can sift through hundreds of thousands of images faster than a human surveillance team, allowing that team to review critical images and respond faster. This is a huge opportunity to reduce the amount of time spend manually reviewing and augment that person’s ability to do their job.

Security

Computer Vision has awesome detection capabilities that can “see” where a car, person, unattended bag, anything that is trained to detect and creates an alert. Bounding boxes enable this cool feature. This is when a box is placed around the object, labels it, and trains the computer to identify it. Pre-trained models (which many Ai firms are limited to) are not editable, which is useful if you just want to identify a pre-set object.

But what if you could take the structure of that model and its learnings and train it on your specific items? All the bounding boxes are chosen for your specific company, making the findings more relevant and generate a lot more value. That’s the impetus beyond what you can test online or with a company limited with just pre-trained models. With ElectrifAi, you can add your own data to fine-tune the model. That’s where the magic happens.

Predictive Maintenance

Computer Vision can help companies such as fruit producers optimize fruit lifespan. When we look an orange and it’s discolored or moldy, we know it’s bad. Computer Vision, though, can look at the pixel level and determine the exact time deterioration begins. With the value of data and predictive analytics, we can create a path to determine how long it takes a piece of fruit to go bad from being picked to selling it at the store.

Conclusion

Want to learn more? Check out fun use case demonstrations about the capabilities of Applied Ai in Video Analytics in the third episode of This Week in Applied Ai.

Imaging taking your videos to the next level with a pre-built machine learning model trained on your data. That’s the power of utilizing a solution like this – a model already feature engineered based on years of domain expertise. Being able to train your data on top of that gives your company countless benefits.

Use work we’ve already done that has proven to be successful. Focus on your core competency and let us do the rest!

Want to check out a custom demo to see how your data can begin to work for you? Contact us today!

You have data, we have solutions!

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