Cepton, Inc.’s (NASDAQ: CPTN) Lidar Surveillance
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- Commonly used surveillance cameras employ AI-powered facial recognition technology that can compromise privacy and lead to biased decisions
- Lidar may hold the key to privacy-focused solutions by feeding purely behavior-based and biometric-free data into systems
- Cepton’s lidar-based solutions are developing substantial traction with pilot systems in-place for airport terminal monitoring, highway e-tolling, early-warning intrusion detection, and railway obstacle detection
- `Cepton is currently engaged in discussions with every top-10 global automotive OEM to provide lidar-based perception solutions for automotive applications (e.g., AV and ADAS systems)
Facial recognition technology is increasingly being used to monitor public and private spaces, leading to widespread concerns that AI-enabled surveillance applications compromise privacy and produce biased outputs.
Dr. Jun Pei, CEO and Co-Founder of Cepton (NASDAQ: CPTN), a Silicon Valley innovator of high-performance lidar solutions, believes lidar holds the key to developing surveillance systems that protect privacy and eliminate AI bias (https://nnw.fm/Vq0LQ ).
Many automated or AI systems learn from human-generated data that may contain prejudicial elements, unconscious bias, and stigma. As a result, algorithms that generate results are only as neutral as the data they receive, prompting experts to question whether autonomous solutions can generate truly unbiased decisions and outputs.
“When it comes to building automation into environments to improve safety, security and efficiency, I believe the answer is yes,” writes Dr. Pei in an article for Tech for Good – an online website that documents how technology solves the world’s most critical challenges (https://nnw.fm/c8dvZ ).
“It depends fundamentally on what data is captured and how it is captured and processed,” reads the article. “A world inundated with cameras has more than just privacy implications – potential AI biases get heavily accentuated. But, with the advent of lidar technology, a new tool is now available to overcome the limitations of camera-only approaches.”
Commonly used camera-based surveillance systems with integrated facial recognition technology typically collect skin color and facial feature data. According to Dr. Pei, this information is not necessary because security applications should make decisions based on behavioral patterns.
In contrast, lidar builds real-time 3D images by sending out invisible, infrared light signals and measuring how long it takes for them to bounce back. Data produced by the system is purely based on behavior, ensuring that privacy is protected, and analytical bias is kept to a minimum. Lidar also provides initial threat verification when used in tandem with security applications, enabling systems to detect objects using lidar before switching on cameras for additional confirmation. One advantage of a lidar-based system over camera is its ability to operate efficiently under low light conditions. Therefore, you wouldn’t need to have a lighting system always on to be able to detect potential intruders or security threats. Lidar is also able to offer sufficient detail, giving analysts insights into crowd density, crowd flow, and how people interact with their surrounding spaces.
Within the automotive marketplace, Cepton is currently engaged in discussions with every top-10 global automotive OEM to provide solutions for automotive applications (e.g., Autonomous Vehicles (“AV”) and Advanced Driver Assistance Systems (“ADAS”)). Other mainstream applications of Cepton’s technology include airport terminal monitoring, highway e-tolling, early-warning intrusion detection, and obstacle detection at railway level crossings. With an optimal balance between performance, reliability, and cost efficiency, Cepton offers superior value and mass-market scalability, enabling its partners to integrate intelligent perception solutions into a wide range of smart applications.
For more information, visit the company’s website at www.Cepton.com.
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