This article is part ofTechXchange：AI on the Edge
您将学到什么：
- Edge AI如何通过无数的新功能和可能的用途来促进智能视频分析（IVA）的新时代。
- 新的Edge AI技术增强处理能力对不同行业的AI辅助视频设备的影响，影响IVA设备可实现的任务的质量和数量。
由于Edge AI技术的最新发展，智能视频分析（IVA）在众多行业中正在上升。AI集成的分析解决方案比比皆是 - 智能摄像机，智能网络录像机（NVRS）和Edge AI框等发动机现在能够进行强大的内置AI处理。
摄像机和其他小型设备的能力识别对象，跟踪运动甚至识别行为首先以有限的容量到达边缘，提供了不可靠的警报和跟踪以及低质量的检测。但是，如今的AI处理器可以每秒（顶部）为现有摄像机和设备带来指数级的操作，并以每秒数千帧（FPS）的数千帧（FPS）运行最先进的神经网络（NN）型号。现在，这些边缘设备可用的这种计算能力曾经专门为云保留。
For proof that mid-range edge AI solutions are entering the mainstream, look at a recent study conducted by Omdia research commissioned by Hailo. The报告估算在2021年，售出的相机和NVR的26％具有AI的能力，并预测，到2025年，AI功能相机将占全球IP摄像头销售的64％。
增强AI的影响
东北的增强的处理能力和效率w edge AI technology has brought tangible improvements to AI-assisted video devices. It impacts both the quality and quantity of the tasks achievable with these devices.
随着延迟较低和较高的帧速率，基于深度学习的任务可以实时在边缘设备上运行。实时帧速率处理允许更具响应性的接口，并且对于必须检测和跟踪快速移动对象的任务尤为重要。
Edge AI also affords high-resolution cameras (UHD, FHD) the large number of TOPS needed to accurately detect small details and provide actionable image analytics. More efficientAI processing power lets AI applications use more complex and therefore more accurate NN models, resulting in better detections and classifications and more reliable alerts.
系统级成本节省代表了Edge AI改进的另一个诱人副产品，因为更强大的相机IVA可以涵盖更多的关注区域（ROI），对公司的底线影响较小。可以轻松地跟踪，记录和传输丰富的元数据和分析见解，而无需昂贵的内放设备存储或基于云的处理。
Quality Assurance Automation
质量保证与具有挑战性一样重要，尤其是对于制造业和工业流程而言。一些研究估计这种缺陷会导致50％的生产浪费，这一数字在制造线更复杂的情况下上升了90％。
Recent innovations have produced highly accurate machine vision that scans batches of product for defects at a volume and level of detail far beyond what the human eye can handle. However, the data-processing capabilities needed for completely automated inspection need to be local—on the production line. This is due to strict latency limits, which are tied to the conveyor belt or other production equipment that operate continuously.
强大的边缘AI可以同时分析非常高的框架摄像机和多个流摄像头输入，从而可以进行更迅速，详细的出厂监控和产品检查。生产率将下降，节省工厂的时间，金钱和材料。
随着AI驱动的计算机处理的改善，视觉智能和感官感知将很快成为行业4.0的新标准。
零售安全性分析
Line-crossing detection has long been the primary method for monitoring footfall of incoming and outgoing shoppers, but it’s not always reliable. AI-integrated cameras allow for more accurate tracking with little to no missed counts by using blind-spot recovery and depth estimation. AI devices also can support automated entry-point detection, which is particularly useful for stores or retail spaces where the entry point is dynamic or not a clearly defined doorway.
Furthermore, AI cameras can provide business intelligence (BI) insights for retailers. What parts of the store attract customers the most? What parts of a store made the most people stop? Intelligent cameras can shed light on these questions and help business strategists derive customer-intent projections or traffic heatmaps, enabling them to reconfigure store layout or displays to maximize profits and enhance the shopping experience. In addition, loss prevention can be bolstered through AI identification of vulnerable or frequently stolen products.
IVA的未来
The robust processing abilities of edge AI are shifting IVA from simple object and motion detection to full-fledged scene understanding and context awareness. The former offers a metadata-rich view of a given ROI, while the latter provides a perception of a single detected object within the spatial and temporal context of a video recording.
随着场景的理解和上下文意识过程不断改善，这些分析设备将越来越能够将意图分析甚至行为分析整合到其应用程序中。
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