Edge AI in Video Analytics and Surveillance System

Introduction to Edge AI

Processing data near the network's edge, where the data is generated, instead of a centralised data-processing warehouse. Edge computing enables mobile computing and Iot technologies. It makes data and devices more affordable and connected without increasing latency. Let’s dive deeply into edge computing and understand how the technology works with Akira.ai's solution for business users.

Edge AI has the potential to revolutionize how AI technology is developed and used worldwide. Click to explore our, Edge AI Architecture and its Applications

Why do we need Edge AI?

Emerging technologies like deep learning and neural networks, which have revolutionary potential depending on cloud computing, hamper their runtimes, increasing massive power requirements. Forrester Research, an International information technology Firm, reports, "Latency is becoming an issue as firms try to push more data to software that runs in the cloud or the data centre.” As the amount of data increases, it becomes more uneconomical to do all processing centrally.

Edge Computing acts as an efficient technology that brings intelligence closer to the place where intelligence is needed and, in return, unleashes the collective power of intelligent devices. As the number of firms grows and increases, central software platforms handling the inflow of data are being pushed to the edge. The primary motivations for choosing Edge Computing are:

• Real-time data processing without latency or delay in the transfer of data.

• Eliminates lag time or allows innovative applications to respond to data instantly.

• A large amount of data is processed near sources, reducing internet bandwidth.

• Eliminates costs, ensuring applications are used in remote locations.

• Processing data without putting it in the cloud adds security for sensitive data.

Use of Edge AI in Video Surveillance Systems

Preliminaries

• Machine learning/Deep learning model: Model

• Edge Device: Raspberry Pi

• Framework to be used: Tensorflow/Tensorflow Lite

Popular Edge Devices and their Hardware specifications (For reference to select a framework according to the Device)

The architecture of Edge AI in Video Surveillance

• The overall architecture of running any model includes the following steps:

• Considering these steps, here is the solution diagram:

• After getting the model ready, here are the steps that will be followed

Applications of Real-Time Edge AI in Video Surveillance 

1. Traffic Management 

Edge AI can analyse real-time video feeds to: 

• Identify traffic congestion. 

• Detect accidents or road blockages. 

• Optimise signal timing for smoother flow. 

2. Public Safety 

Edge AI enhances public safety by: 

• Detecting potential threats in crowded areas. 

• Enabling facial recognition for identifying wanted individuals. 

• Providing instant alerts for unattended objects. 

3. Retail Analytics 

Surveillance systems equipped with Edge AI provide: 

• Insights into customer behaviour for personalized marketing. 

• Real-time alerts for theft or fraud detection. 

• Optimization of store layouts based on heatmap analysis. 

4. Smart Cities 

Edge AI supports the development of connected urban environments through: 

• Intelligent lighting and waste management. 

• Automated incident detection in public spaces. 

• Enhanced environmental monitoring. 

5. Healthcare 

In hospitals and clinics, Edge AI: 

• Monitors patients for falls or unusual activity. 

• Ensures compliance with safety protocols. 

• Detects unauthorized access to restricted areas. 

 

Overcoming Challenges in Edge AI for Video Surveillance 

Computational Limitations 

• Challenge: Edge devices often have limited processing power. 

• Solution: Use lightweight AI models optimised for edge deployment, such as quantised neural networks. 

Model Optimization 

• Challenge: Training and deploying deep learning models for real-time performance. 

• Solution: Utilise hardware accelerators like GPUS and TPUS for faster inference. 

Environmental Variability 

• Challenge: Lighting, weather, and camera placement variations can impact model accuracy. 

• Solution: Train models with diverse datasets and implement adaptive algorithms. 

Future of Edge AI in Video Surveillance 

As Edge AI evolves, improvements in hardware and software will create new possibilities.

• Federated Learning: Enabling edge devices to collaboratively learn from data without centralising it, enhancing privacy. 

• Self-Optimising Systems: Surveillance systems that automatically adjust settings for optimal performance. 

• Integration with Iot: Combining video analytics with Iot sensors for comprehensive monitoring solutions.

Real-time Edge AI is transforming video surveillance by providing AI-driven insights at the edge. This technology enhances traffic management in smart cities and strengthens security in public spaces. Despite challenges, some advancements to deep learning models, optimized architectures, and edge hardware are leaving the door open for intelligent, efficient, secure surveillance systems. With Enterprise investment in Edge AI, the new frontier in security and operational efficiency will emerge as robust, adaptive, and scalable solutions are developed to redefine the future of security and operational efficiency in industries across the board.