Edge AI and Multi-Sensor Cameras Dominate 2025 Video Surveillance Tech Landscape: Survey Says

In asmag.com’s latest video surveillance technology survey for 2025, edge AI and multi-sensor cameras emerged as frontrunners, boasting high scores in both suitability and maturity, reflecting their increasing benefits and technical sophistication. While natural language video search is generating buzz in the industry, it received a more moderate ranking in the survey. Here’s a closer look at the findings:

Edge AI Takes the Lead

Edge AI, which integrates AI-powered analytics directly into the camera for tasks like facial and license plate recognition, topped the charts in the survey. It achieved impressive scores of 4.42 for suitability and 4.03 for technical maturity (on a scale of 1 to 5, with 5 being the highest).

“Edge AI has truly matured within the security industry, transitioning from a mere concept to a practical, everyday tool. We have been deploying edge AI for over five years, and it’s now integrated across our entire camera range, even our most affordable models. This eliminates the need for customers to choose between AI or no AI – intelligence is simply a built-in feature,” explained Philippe Henaine, Manager of Strategic Partners at i-PRO.

Henaine emphasizes the practicality of edge AI, stating, “Its appeal stems from its ability to deliver instant analytics directly on the device, reducing bandwidth usage and server expenses, all while maintaining localized data processing for enhanced privacy. Edge-based analytics streamline real-time alarm monitoring and forensic search, enabling operators to identify and review events with unparalleled efficiency. In essence, edge AI is a dependable, scalable technology, ideal for a wide range of security projects.”

Multi-Sensor Cameras: A Cost-Effective Solution

Multi-sensor cameras, which consolidate multiple sensors into a single unit, also garnered high marks in the survey. They achieved a score of 4.18 for suitability and 3.91 for technical maturity. This strong performance is largely due to their inherent advantages: Users can replace multiple single-direction cameras with a single, multi-directional unit, reducing costs and licensing fees. This makes multi-sensor cameras an excellent choice for comprehensive surveillance of expansive areas like warehouses and parking facilities. Recent technical advancements, such as the integration of all sensors onto a single system-on-chip (SoC), further reduce power consumption and simplify network configuration. Image stitching, previously plagued by visible seams and exposure inconsistencies, has seen considerable improvements thanks to real-time blending, HDR synchronization, and automatic exposure balancing. With these advancements, the demand and growth of multi-sensor cameras are projected to continue.

VSaaS: A Balanced Perspective

Video Surveillance as a Service (VSaaS) occupied a moderate position in the survey, scoring 3.98 for suitability and 3.63 for technical maturity. The survey acknowledges that cloud-based video surveillance has its limitations. High bandwidth consumption, especially with high-resolution video, poses a significant challenge, making VSaaS less suitable for remote locations and multi-camera deployments. The challenges of real-time monitoring and analytics processing over the internet, combined with potentially high costs associated with continuous recording from numerous cameras, also contribute to its more moderate ranking compared to edge AI. Nevertheless, the benefits of VSaaS, including scalability, centralized management, and reduced capital expenditure, ensure its continued viability. Hybrid solutions that combine cloud and edge technologies offer an even more compelling alternative for users.

Natural Language Footage Search: Promising, But Still Developing

The rise of natural language processing tools like ChatGPT has sparked interest in natural language footage search within the security industry. Instead of manually selecting search criteria, users can simply type commands like, “Find a person wearing a red shirt and blue jeans between 2 and 3 pm.” While this technology has been prominently featured at recent security trade shows, it received a moderate ranking in the survey, scoring 3.92 for suitability and 3.2 for technical maturity.

Henaine suggests that the reliance on cloud-based processing in many current solutions is affecting adoption. “Many end-users in the security industry remain cautious about transferring sensitive video data to the cloud due to privacy, compliance, or infrastructure concerns,” he explains. “Therefore, we’ve focused on delivering on-premises free-text search, which operates entirely without internet connectivity. This approach provides our customers with peace of mind. Furthermore, our technology enables existing edge AI cameras to be updated to support these new capabilities, eliminating the need for expensive hardware replacements.”

Vape Detection: Limited Suitability

Vape detection was a new addition to this year’s survey, and it received the lowest scores, with a mere 3.49 for suitability and 3.09 for maturity. The survey suggests that video surveillance alone faces limitations in accurately detecting vaping. Challenges include distinguishing vape from smoke, breath, and phones, leading to potential false positives or negatives. Privacy concerns associated with deploying AI video surveillance for vape detection also contribute to the low score. Alternative solutions, such as dedicated vape/smoke detectors installed in privacy-sensitive areas, may be more suitable. Verkada and Halo are examples of companies offering solutions in this space.