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Advancements In Edge AI Ushering Evolution In Video Analytics

<p>Rapid developments in edge AI pave the pathway for dramatically improved intelligent video analytics by enhancing on-device capabilities and reducing dependency on complex IT backends.</p><p>The current landscape is changing significantly with enhanced capabilities of edge devices such as surveillance cameras and other AI-enabled edge-based endpoints. The state-of-the-art equipment comes with embedded detectors to detect, track and analyze any moving objects. These devices have the capability to detect false alarms and suppress them in a time-sensitive manner.</p><p>Such scene analysis, comprising of identification and classification of objects by edge-based devices, has inherently been poor, marred by low-quality detection and unreliable alerts. But today, these edge devices are equipped with powerful computing and run on neural networks with thousands of frames per second.</p><p>Today&rsquo;s advanced devices come with numerous capabilities such as detecting, loitering, object detection, object count, object classification, speed detection, ANPR (Auto Number Plate Recognition), wrong direction driving, PPE detection, helmet detection, object filtration based on size, speed, direction and color, and much more.</p><h2><span style="font-size: 14pt;">Building the Foundational Blocks with Next-Gen AI </span></h2><p>Significant improvements in the quality and quantity of the output delivered by video tools are driven by a generational leap in the new edge AI&rsquo;s computational efficiency and accuracy.</p><p>Be it real-time deep-learning on the back of higher frame rates and lower latency or on-device processing capabilities, AI-driven enhancements are evident across the board. Not just that, edge AI is also facilitating more precise detection of fast-moving objects, small details, and subsequent provision of insightful analytics.</p><p>Powered by higher processing efficiency and more advanced intelligence models, detection, classification, and notification capabilities are better performing, quick, and significantly more accurate.</p><p>Usually, better performance is closely associated with higher costs. But with edge AI&rsquo;s ability to overcome on-device storage or cloud-based processing through on-premise analytics, businesses not only get better capabilities but also at a reduced cost.</p><h2><span style="font-size: 14pt;">Future Focused</span></h2><p>Comprehensive capabilities rendered by edge AI are quickly repositioning video analytics from object and motion detection to smart scene analysis with intelligent understanding and contextual insights. With an increased focus on data and the need for more comprehensive and actionable screening, the winning IVAs must incorporate the next generation of use cases, such as intent and psychographic analysis, in addition to already advanced existing systems.</p><p><span style="font-size: 10pt;"><em>This article was contributed by our expert <a href="" target="_blank" rel="noopener">Ravindra Singh</a></em></span></p><p>&nbsp;</p><h3><span style="font-size: 18pt;">Frequently Asked Questions Answered by Ravindra Singh</span></h3><h2><span style="font-size: 12pt;">1. Where is edge AI used?</span></h2><p>Edge AI is the latest concept for real-time data capturing and analysing near the source as compared to the previous scenario where the computing was done at the captive data centre or on the cloud.</p><h2><span style="font-size: 12pt;">2. What industries use edge computing?</span></h2><p>Edge computing has multiple applications and a knowledge base can be created based on the industry's needs. Some of the major applications are intelligent Energy management by the smart Grids, Condition based forecasting of machines, AI-based response management in the retail or contact centre, Intelligent Traffic management systems, Object identification, Object Count &amp; Object classification etc.</p><h2><span style="font-size: 12pt;">3. Why is edge computing used in telecommunication networks?</span></h2><p>Most telco companies moving towards computing on edge to provide a better customer experience of applications to end users. If the computer is moved to the edge this avoids latency on the network.</p><h2><span style="font-size: 12pt;">4. What are the benefits of video analytics?</span></h2><p>Compared to the previous video surveillance systems where analysis of any event was done post-incident and was time-consuming, today&rsquo;s systems with edge base analytics or server-based analytics are proactive in reporting an incidence and are real-time. With real-time data, one can actually predict the future and prevent any untoward incident.&nbsp;</p><p>&nbsp;</p>
KR Expert - Ravindra Singh

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