Vision Engineering
Development of Edge AI Cameras for Urban Surveillance
Client Background:
Our client, an acknowledged urban surveillance provider, offers innovative video surveillance systems to governments, law enforcement, and private businesses. They struggled with outdated camera systems that lacked real-time processing and analytics. As urban surroundings became increasingly complicated, companies required an adaptable approach to provide immediate, actionable information.
Regami Solutions was assigned to develop an Edge AI camera system to upgrade its surveillance infrastructure, providing real-time analytics and scalability for modern urban environments.
Challenge:
The client's previous surveillance system struggled with real-time analytics, leading to delays in decision-making, higher operational costs, and inefficiencies in responding to incidents. Without local data processing, the cameras faced bandwidth limitations and slow transmission speeds. Additionally, issues with high latency and low detection accuracy hindered the system’s ability to identify threats or incidents effectively.
The client approached Regami Solutions for an advanced Edge AI camera solution to improve processing speed, detection accuracy, and real-time decision-making.
Our Solutions:
At Regami Solutions, we designed and implemented a comprehensive Edge AI camera system tailored to urban surveillance needs.
Real-time AI-powered Analytics: Our Edge AI cameras are equipped with local processing capabilities to run real-time video analysis directly at the edge. This drastically reduces latency, enabling faster incident detection, such as traffic violations or public disturbances, and allowing authorities to respond swiftly and effectively.
Scalable for Urban Environments: Our solution is built to scale efficiently across large urban areas with diverse infrastructure needs. Whether installed in busy city centers or expansive urban environments, the cameras integrate into a broader network of connected devices, providing wide-ranging surveillance coverage across city streets, public spaces, and highways.
Enhanced Object Detection Accuracy: Using advanced deep learning algorithms, our Edge AI cameras achieve exceptional accuracy in detecting and classifying objects such as vehicles, pedestrians, and bicycles, even under challenging conditions like low light or inclement weather. This ensures reliable detection and enhanced situational awareness.
Cloud and Edge Hybrid Integration: Our hybrid cloud-edge architecture allows the system to process critical data locally for low-latency tasks while offloading less urgent data to the cloud for further analysis and storage. This optimizes bandwidth usage, improves system efficiency, and ensures that the system remains responsive under varying load conditions.
Advanced Video Analytics: The cameras feature modern video analytics, including facial recognition, license plate recognition (LPR), and behavioral analysis. These capabilities not only enhance security but also provide valuable insights into traffic patterns, potential security threats, and urban dynamics.
Over-the-Air Updates with ROTA: With our ROTA platform, the system supports seamless remote management and over-the-air software updates, ensuring that cameras stay up-to-date with the latest features and security enhancements without the need for on-site maintenance. This keeps the system agile and ready to evolve with future technological advancements.
Outcomes:
These are the key outcomes achieved with the deployment of the Edge AI camera system, which significantly enhanced the client’s urban surveillance capabilities:
Accelerated Incident Reaction: Real-time AI-powered analysis facilitated quick decision-making, drastically lowering reaction times and improving incident response capabilities, resulting in increased public safety.
Optimized Latency and Bandwidth Efficiency: Local edge processing diminished data transmission latency and bandwidth usage, resulting in increased surveillance efficiency and cost-effectiveness.
Enhanced Detection Accuracy: Advanced object detection capabilities enabled precise identification of potential threats, such as unauthorized vehicle movements and public disturbances, improving system reliability.
Seamless Scalability: The system's flexible deployment allowed the client to easily expand their surveillance network, ensuring high performance and operational efficiency in response to evolving urban challenges.
Cost Savings in Network Management: The hybrid edge-cloud architecture reduced operating costs by optimizing data flow and reducing the requirement for costly cloud storage and bandwidth.
Future-Proof System: With the ability to update remotely and operate cameras, the system can now adapt to future technology breakthroughs and urban demands, assuring long-term viability.