TABLE OF CONTENTS

AI: The next evolution of video surveillance

AI is changing video surveillance. Learn how. | Spot AI

By

Matt Klingbeil

in

|

Table of Contents

There are over one billion security cameras in the world now, over double the number of cameras there were in 2015.

As access to video has become more prevalent, more people are using video frequently and for much more than just security. Video use is no longer limited to reactive use by just security or IT. Professionals across the organization are using video in their day-to-day work. Today, school principals and superintendents are using video to stop student vaping and ensure better safety across campus. Operations teams at manufacturers are using video to optimize production workflows, and retail loss prevention teams are reducing shrink and setting up better processes for merchandise placement.

Camera systems have historically been used as reactive tools - searching for footage, analyzing it, and sharing it after an incident already occurs. But with AI, camera systems are rapidly becoming powerful proactive tools, preventing incidents from happening in the first place.

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Open source AI foundation models have changed video surveillance

Early AI models were hyper-tuned to specific use cases. Building the models was difficult and time-consuming because it required gathering large amounts of data, tagging the data, training the model on the data, and fine tuning the data to generate the desired results.

The dawn of open source AI foundation models has changed the landscape. These foundation models have unique characteristics:

  • Powerful - Using various methods of gathering vast datasets, like crowdsourcing, web scraping, data sharing, and simulated data, foundation models were built and made publicly available.
  • Multimodal - Foundation models can understand and process information from multiple modalities or sources of data, such as text, images, videos, audio, and other forms of sensory inputs.
  • Self-learning -  Technological advances have also allowed AI models to develop self-learning capabilities where the models continuously learn from inflowing data and improve their performance over time without explicit programming.
  • Accessible - Anyone in the world can access these powerful models and use them to solve real-world problems, leading to a surge in AI startups founded since the late 2010s.

Foundation models are driving an AI revolution in every sector of the economy

AI technologies are rapidly becoming ingrained in the fibers of our personal and professional lives. Models like ChatGPT have gone mainstream and become so powerful that they are able to function like a live and helpful human assistant. With a simple one-line prompt, ChatGPT can write an entire essay in seconds, complete with cited sources. Generative AI models like Midjourney can generate completely unique works of art nearly instantly when only provided with a single phrase.

How different industries use AI

Schools are leveraging AI-powered educational software to tailor lessons to the needs of individual students by adapting the level of difficulty based on the student’s performance, ensuring that each student can learn at their own pace. Healthcare companies are using AI to simulate and predict the interactions between molecules in order to accelerate drug discovery. And transportation companies are using AI to optimize delivery routes and reduce fuel consumption and delivery times based on real-time traffic patterns and weather conditions.

AI is unlocking new operational use cases of video that were never before possible.  

Just some examples of the AI revolution across the economy include:

  • Schools leveraging AI to drive campus safety and reduce vaping incidents
  • Manufacturing companies using AI to improve worker safety, operational efficiency, and production monitoring
  • Retail companies relying on AI to accelerate loss prevention, monitor employee productivity, and track merchandise placement

The advent of foundation models has also unlocked the ability for camera systems to analyze the physical context of business environments and intelligently provide valuable visual context without the hassle of brute-forcing through a dated system.

Not only is AI driving incredible and proactive automation of traditional security workflows, it is also unlocking new operational use cases at a level never before possible that help businesses generate more revenue than they previously could by automatically detecting areas of inefficiency, providing prompts on how to address them, and providing professionals in every organization insights and analytics from video data to support their day-to-day objectives.

The immense impact of AI is already being felt, but is still largely untapped. We are at the forefront of an AI revolution that will impact every sector of the economy.

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