Support smarter decision-making with video analyticsHPE Edgeline solutions for video analytics at the edge

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What is artificial intelligence (AI)?AI is a process where a computer solves a task in a way that mimics human behavior, but often looks beyond parameters that humans typically use to subsume all available data. Narrow AI—when a machine is trained to do one specific task—is becoming more widely used, from virtual assistants to self-driving cars.

What is machine learning (ML)?ML is an approach to AI that uses statistics techniques to construct a model from observed data, using algorithms that enable computers to learn from examples without being explicitly programmed.

What is deep learning (DL)?DL is a subset of ML, using deep artificial neural networks as models and requiring no feature engineering.

Why are those techniques important?Organizations want to explore and exploit the massive data sets produced every day to enable:

• Self-driving vehicles

• Robotics automation

•Medical improvements

• Security and fraud detection

• Advertising and home applications

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Based on deeper, faster, more accurate insightOnce a mainstay of financial institutions and retail stores to reduce loss/theft, video is increasingly being used to automate analysis of a number of events from people, places, and things:

•Public safety

•Physical security (facility/perimeter protection)

•Quality assurance

•Employee/contractor/event safety

•Traffic optimization and many more

To be implemented successfully, massive amounts of data gathered from video cameras must be analyzed in a timely manner. Both the size of the data sets and the urgency required to make decisions can create challenges for existing video analytics infrastructures.

Current surveillance systems rely on human operators and use video synopsis to determinate what happened in the past. Such latency and possibility of human errors are no longer acceptable. A new approach is needed to enable cities, manufacturers, retailers, financial institutions, and public venues to quickly and accurately analyze video.

New analytics techniques, such as machine learning and artificial intelligence, require increased connectivity, storage, graphics accelerators, and computing capabilities to analyze video images in near real-time in environments where cameras are found (hot, dusty, and power-limited).

In response to this need, HPE designed Edgeline Converged Edge Systems to be able to connect, analyze, and act on video data. These all-in-one systems leverage artificial intelligence, machine learning, and deep learning to process data at the edge. By processing data where it is generated, in less time, with superior accuracy, these high-performance systems can overcome:

•Delays that prevent real-time determination of events as or before they occur

Video is becoming a universal IoT sensor for providing physical security, as well as ensuring asset tracking, quality assurance, and worker safety. Video cameras create enormous amounts of data, which must be captured and analyzed at the edge when events occur, rather than after the fact.

“The edge is increasingly becoming a centerpiece of the digital enterprise where things and people generate and act on massive amounts of data. Our edge-to-cloud solutions help bring enterprise-class capabilities from the data center to the edge. This reduces software and IT administration costs, while accelerating insight and control across the organization and supply chain.”– Dr. Tom Bradicich, Vice President and General Manager, IoT and Converged Edge Systems, HPE

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•Bandwidth challenges due to transferring high-resolution big video data across cities, countries, or continents to central data centers or clouds

•The cost of storing ever-growing volumes of video data

•Challenges of data sovereignty

The HPE Edgeline Video Analytics portfolio was built to address these issues. These solutions are based on HPE Edgeline Converged Edge Systems, powered by Intel® Xeon® processors, and backed by HPE’s industry-leading security and video analysis partners.

Accelerating video analytics and action at the edge

Designed to withstand space restrictions and harsh, hot, and dusty conditions, HPE Edgeline systems are able to extract the value of video data generated at the edge. Using compact and power-efficient HPE Edgeline systems, organizations in the private and public sectors benefit from:

•Faster time to action and control. With a high concentration of GPUs, HPE Edgeline systems enable analysis of events as they occur in real-time, so people can take immediate corrective action.

•Lower bandwidth utilization. Avoid unnecessary video data transfers; use costly bandwidth more efficiently and transfer only the data or metadata that is relevant.

•Lower costs. Drive down costs by analyzing the video near the camera and saving on the costly network infrastructure and storage required by a cloud or data center-only solution.

•Enhanced data security. With fewer transfers to and from the central data center, data remains at the edge and not exposed to security breaches.

•Improved reliability. By managing data locally, the risk of data corruption during transfer is lowered; data is computed at the edge rather than at a distant data center or cloud.

•Streamlined management. Get enterprise-class system management at the edge with HPE Integrated Lights Out (iLO 4).

•Data policy and compliance. Maintain data sovereignty with local standards of data usage and transfer by keeping data local.

Why HPE Edgeline systems for video analytics at the edge?

•Purpose-built for the edge. In addition to handling high shock and vibration levels, these rugged data center-class systems are designed to withstand environmental conditions from 0°C to 55°C during full operation.

•Tested and certified software. A multitude of industry-leading software stacks specialized in security and intelligent video analytics (IVA) and management are tested and certified on HPE Edgeline Converged Edge Systems. HPE helps with sizing guidance, selecting suitable IVA vendors, and reducing implementation time.

•Comprehensive services for the Intelligent Edge and IoT. HPE Pointnext leverages broad and deep technical expertise and innovation to help accelerate digital transformation and support companies as they move to edge computing. A comprehensive portfolio of operational, advisory, and professional services helps enterprises evolve and grow today and into the future.

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HPE Edgeline EL1000 Converged Edge SystemEntry-level infrastructure features a single compute blade—HPE ProLiant m510 (Intel Xeon D—8 or 16 cores each) or HPE ProLiant m710x (Intel Xeon E3— 4 core with embedded GPUs)—with two data capture/control slots and multiple I/O and storage options.

HPE Edgeline EL4000 Converged Edge SystemDense and scalable infrastructure that features up to 4 compute blades—HPE ProLiant m510 (Intel Xeon D—8 or 16 cores each) and/or HPE ProLiant m710x (Intel Xeon E3—4 core with embedded GPUs); the perfect infrastructure for running parallel applications.

HPE Edgeline Extended Storage Adapter The HPE Edgeline Extended Storage Adapter option kit adds up to 4 TB per adapter of software-defined storage to HPE Edgeline Converged Edge Systems. This system enhancement enables storage-intensive use cases such as artificial intelligence, video analytics, or databases at the edge, while also leveraging industry-standard storage management tools such as Microsoft® Storage Spaces Direct, HPE StoreVirtual VSA, and VMware vSAN™.

To learn more about the latest addition to the HPE Edgeline product portfolio, please visit hpe.com/info/edgeline.

HPE IoT GatewaysOptimally configured with CPU, memory, connectivity, and an expansive I/O selection to address a host of IoT needs; choose from the HPE GL10 or GL20, based upon process and I/O requirements.

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HPE Edgeline video analytics solution in action

By deploying the HPE Edgeline for video analytics, organizations can improve customer experiences, retain more customers, improve service and product quality, and enhance security.

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Learn more athpe.com/info/edgelineHPE Converged Edge Systems Video Analytics and Management Solution

Desired outcome Insights required Attributes to detect

Improving customer experiences/retention

Identify and satisfy high-value and repeat customersOptimize staffing based on trafficOptimize floor plans based on traffic patternsGather customer demographicsImprove billing procedures

Facial detectionEmotional analysisPeople countingStatistics from heat maps/traffic mapsObject attribute detectionLicense plate recognition

Enhancing security Authenticate approved personnelDetect intrusion from locked entrances after hoursDetect fraudulent behaviorIdentify suspicious behaviorDetect illegal parkingIdentify abandoned objects/vehicles

Facial detectionAnomaly detectionObject trackingIntrusion detectionVehicle tracking and license plate recognitionObject detection

Optimize production process

Process controlQuality auditPredictive maintenance

Material deformation monitoringQuality inspectionAnomaly detection

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© Copyright 2018 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions contained herein.

Intel Xeon and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Microsoft is either a registered trademark or trademark of Microsoft Corporation in the United States and/or other countries. VMware vSAN is a registered trademark or trademark of VMware, Inc. in the United States and/or other jurisdictions. All other third-party marks are property of their respective owners.

a00050140ENW, September 2018