From people counting to augmented reality - The revolution of Video Analytics vs Lidar sensors.
It is increasingly evident that we live in a world in which the border between the physical and digital environments is blurring. In this panorama, two technologies shine: Lidar sensors and computer vision, especially when combined with Artificial Intelligence (AI). Both share a common goal: transform our perception of spaces and the people who inhabit them.
Ultra-precise depth maps: Lidar
Imagine a beam of light that moves at a frenetic pace, measuring distances with unmatched precision andgenerating a super detailed map of spaces and the people in them. That is the essence of Lidar. This technology, used in climate research and mapping, is now also being used to count people. But its accuracy comes at a price, with costs ranging between $1,000 and $10,000 per device, which can limit its implementation in some cases.
The eye of Artificial Intelligence: Computer Vision
On the other side of the ring, we have AI computer vision, an evolution of traditional cameras. With much lower costs ($50 to $500 per camera), this technology can view, understand and analyze images or videos in real time, allowing not only to count people, but also to understand their behavior and interactions.
In this technological race, AI vision has a much faster development. The number of internationally recognized papers with new AI techniques for computer vision reaches 30 innovations per year. In the case of Lidar, there are 3 innovations per year.
Computer vision AI has 30 per year, while Lidar AI has 3 per year.
You can learn more about this advance in this report on multimodal generative AI: present and future.
Road to optimization and savings
The battle between both technologies is not limited to their technical capabilities. When we talk about economic scalability, computer vision wins resoundingly: a $1,000 PC can analyze up to 50 cameras, compared to the cost of installing 50 Lidar sensors.
In addition, security cameras are normally already installed in the spaces, so reinvestment in infrastructure and new installations, wiring, etc. is not required.
Applying computer vision is between 10 and 100 times cheaper than Lidar technology
But what happens in the streets? How is Lidar Evolving Vs Computer Vision AI?
And what is happening in the industry on the issue of Lidar people counting vs Video Analytics? The best example of this technological confrontation occurs in the field of autonomous driving. Here, Tesla decided to bet on computer vision for its Autopilot system, opting for agility and flexibility over the cost and complexity of Lidar. However, in other applications such as surveying or archaeology, Lidar remains unmatched.
Currently Tesla is the only company that has managed to implement a large-scale autonomous driving model in an economically viable way
The future: Digital twins with AI
One of the most impactful applications of computer vision with AI is the creation of digital twins. KSI VISION, for example, generates real-time digital replicas of physical spaces, which allows to effectively monitor the flow of people in stores, shopping centers, airports and passenger terminals.
In short, although Lidar and computer vision with AI may seem diametrically opposed, they represent two complementary ways of understanding and optimizing our physical spaces. In this duel, the true victory is in the hands of those who know how to combine and apply these technologies strategically.
If you want to know more about how you can implement digital twins in your industry, do not hesitate to contact us at this link.