AI’s growing importance in video surveillance stems from its transformative impact on enhancing system capabilities and efficiency. Traditionally, video surveillance involved using cameras to record footage, which was largely used post-factum to detect intrusions or deter criminal activity. However, with emerging AIoT technologies, there is a shift towards using computational power and advanced algorithms to analyze video feeds in real-time. This enables systems to autonomously interpret and react to the captured footage, significantly enhancing the utility and effectiveness of surveillance systems.
AI in video surveillance facilitates diverse functionalities such as facial recognition, object detection, and identifying safety concerns like fire or dangerous behavior. A wide array of intelligent recognition algorithms can now identify hard hats, uniforms, high-altitude object throws, and more. Nevertheless, these AI algorithms demand substantial computational power. The process involves decoding video footage frame by frame and using sophisticated comparisons to deduce conclusions. Such analysis requires robust processing capabilities, typically necessitating GPUs to handle the workload effectively. This demand escalates the performance requirements and costs associated with video surveillance hardware.
Moreover, deploying these capabilities poses a significant challenge. The surge in demand for AI-driven analysis in the surveillance sector has made it crucial to address how computational power is distributed and managed within these systems. This underscores the importance of integrating AI on the camera itself, optimizing real-time analysis and feedback, and streamlining the entire surveillance process.
Deployment Methods of AI Computing Power in Video Surveillance
In video surveillance, AI analytics can be deployed through three primary methods: cloud-based processing, edge computing, and on-camera analytics, known commonly as cloud, edge, and endpoint solutions. Each of these methods has its own advantages, but recently, there has been a significant trend towards integrating AI capabilities directly within cameras. Here’s why:
AI Algorithms in the Cloud
Deploying AI algorithms in the cloud can be complex, as it involves separating the analytics from the camera. The first challenge is efficiently streaming surveillance videos to the analytical servers. Most AI algorithm companies excel in algorithm development but may lack robust capabilities for handling streaming inputs, leading to complex configurations and issues like unsuccessful video integration in projects.
AI Algorithms on the Edge
Some edge computing solutions retrieve streams directly from cameras. While this approach was initially feasible, the modern demands on video surveillance have increased dramatically. AI analytics need to access video streams 24/7, putting significant pressure on cameras. This can result in failed streams, camera downtime, black screens, and instability due to improper stream handling. The more efficient solution involves using video access gateways to manage streams, distributing them to various platforms, including AI analytics servers.
Note:
Both cloud and edge deployments consume significant bandwidth, especially when limited algorithms are deployed in the cloud. Real-time analysis would demand excessive uploading bandwidth, which many surveillance projects cannot sustain, thus affecting the quality of analysis.
Also read: which factors have made edge computing cheaper and easier?
AI Algorithms on the Cameras
Deploying AI algorithms directly within cameras leverages their own computing power for analysis, returning only the processed results, thereby sidestepping the above issues. This approach offers other two key benefits:
Reduced Hardware Costs
Early cameras had limited configurations focused solely on video encoding, lacking analytical capabilities due to cost concerns. Integrating AI capabilities required more advanced, costly hardware. Edge computing devices also utilized camera feeds for external analysis. However, as AI’s market scale expanded and hardware costs decreased, many camera manufacturers began integrating better hardware and AI capabilities into cameras themselves, creating competition with edge computing solutions.
Higher Sensor Integration
A significant challenge in AI video analysis is improving accuracy, which requires algorithm optimization and learning, often at high costs. Video analysis alone may not meet accuracy requirements due to its limited perspective. By integrating additional sensors (e.g., PIR, temperature, smoke), combined with video analysis, accuracy can significantly improve.
For instance, flame detection is more effective when sensor data is integrated with video input. Cameras with built-in sensors offer a substantial advantage in industry applications. Unlike cloud or edge solutions, which face difficulties integrating sensors due to separate deployments and the need for IoT gateways, integrated cameras solve multiple problems with a single device, enhancing AI recognition accuracy and meeting market demand.
By embedding AI analysis directly in cameras and integrating additional sensors, video surveillance manufacturers are innovating to meet the dynamic needs of the industry, garnering market support for these advanced solutions.
Introducing Dusun IoT’s RK3588 AI Camera Motherboard
Dusun IoT’s RK3588 AI Camera Motherboard is designed to meet the evolving needs of camera manufacturers and align with current trends in AI-driven surveillance, this solution offers:
- Support for AI Frameworks: The RK3588 supports popular deep learning frameworks such as TensorFlow, MXNet, PyTorch, and Caffe, enabling easy deployment of AI models.
- Advanced Video Processing: It excels in video encoding and decoding, supporting up to 8K@60fps. This allows for high-efficiency processing of video content with capabilities for both encoding and decoding multiple channels simultaneously.
- Robust GPU Power: The integrated ARM Mali-G610 GPU offers 450 GFLOPS of processing power, making it suitable for demanding graphic processing and image manipulation tasks.
- Complete Interface Options: The motherboard includes diverse camera interface options, such as MIPI, USB, and Ethernet, providing flexibility for various configurations and sensor integrations.
- Infrared Night Vision Support: It features a connector for infrared night vision, enhancing visibility and image quality in low-light or adverse weather conditions.
- High-Speed Connectivity: Equipped with Gigabit Ethernet, LTE CAT4 module support, eSIM, GPS, and SIM card slot, it offers reliable and versatile network connectivity options.
- Low Power Consumption: The design is optimized for power efficiency, supporting longer operation periods with minimal energy usage, which is crucial for continuous surveillance operations.
- Onboard Lithium Battery Connector: In case of power outages, the lithium battery connector ensures uninterrupted operation, making it particularly reliable for security and surveillance applications.
- Rigorously Tested for Reliability: The motherboard is tested to operate optimally in harsh environments, ensuring stability and reliability in various deployment conditions.
These features collectively make the RK3588 AI camera motherboard a powerful and flexible solution for advanced video surveillance systems, capable of handling multiple sophisticated tasks while maintaining efficiency and reliability.
Embracing the Future with Dusun IoT’s Cutting-edge AI Camera Motherboard Solutions
The increasing popularity of AI cameras in the surveillance industry underscores the vital role of advanced analytics in security and monitoring applications. AI analysis of videos will become more and more popular, solving users’ practical problems and improving efficiency. The three application scenarios of video analysis will also have their own markets, giving full play to their advantages and better serving customers.
Dusun IoT is at the forefront of this transformation, providing cutting-edge solutions that include the RK3588 AI camera motherboard. This innovative platform not only meets the high demands of modern surveillance but also simplifies the development process by offering complete BSP and SDK support. Furthermore, with dedicated technical engineering assistance, Dusun IoT ensures a seamless integration experience, empowering manufacturers to create intelligent camera systems that are both efficient and effective in addressing today’s security challenges.
