AI AND VIDEO ANALYTICS BLOG
Video Surveillance & Physical Security Industry Viewpoints
August 28th, 2019
Author: Lizzi Goldmeier

What Hardware Do I Need to Deploy Video Content Analytics?

Comprehensive VCA for Diverse Business Types

Video content analytics is a powerful solution for any organization looking to maximize its investments in video surveillance and extract meaningful intelligence from video data. Whether its law enforcement accelerating investigations and detecting video evidence; shopping center or mall security teams configuring real-time alerts to proactively monitor for suspicious behavior; bank managers looking to intervene before congestion and queues deter customers from transacting or cities trying to quantify traffic and identify navigation patterns for improving transport and traffic flows, video analysis can drive security and efficiency for a variety of organizations. Because video intelligence software is applied so broadly – and so differently by each type of user – the requirements for each deployment are unique and dependent on the individualized needs of the implementation.

That being said, for organizations looking to deploy video content analytics, there are some guiding principles that can help you understand what enabling hardware and technologies you will need to support a video analytics implementation. Here are some of considerations to take into account before integrating a video content analytics platform:

Video Footage Quality

Will you be processing video directly from live cameras or from recorded footage? Or both? What is the quality of the video you’ll be processing? These are important questions you need to answer when integrating video analytics – the answers will determine the kind of hardware you need to support the processing and how demanding the processing will be.

The camera type, for instance, might determine the resolution of video that has to be processed, and – equally as important – the camera placement could affect the types of analysis possible with existing hardware. If a high-resolution camera is installed at eye level, for example, an organization could use it for facial recognition, because the video quality and camera location are likely sufficient for capturing images that can be used for face matching. Your video analytics solution provider might also need to know about the video bitrate and frame rate to prescribe the supporting hardware for your integration.

Video Management Systems

For live video processing and real-time alerting and video search, you will need a Video Management System (VMS) to support your video content analysis. You may utilize several VMS platforms or a single provider. You could opt to leverage the integrated video analytics offered by your VMS solution or to implement a more comprehensive video content analytics platform that seamlessly and flexibly integrates with your existing infrastructure.

GPUs for Video Processing

In the last few years, video content analysis has rapidly evolved with the introduction of Deep Learning techniques enabling the identification, extraction and cataloging of video metadata for analysis applications. Deep Learning has become the standard video analysis enabler, but this has only become possible due to advances to GPUs (Graphic Processing Units), processors with superior computing power to transform video content into actionable intelligence.

Modern video content analytics solutions rely on GPUs and Deep Learning to structure video data and analyze its rich metadata, which is indexed to drive video search, dashboard visualizations and live alerting. When integrating a video analysis solution, your provider will help you understand the number of GPUs you will need to support your video processing requirements in the most cost-effective and efficient ways. These requirements can be affected by factors, such as the number of concurrent users leveraging the system, whether real-time or post-event processing is needed and more.

Surveillance Deployment Model and Scope

Another critical variable for defining video content analysis processing requirements is the size and scope of the organization’s existing video surveillance infrastructure. The needs of a small gas station with a handful of cameras will be different from an international banking institution that wants to draw insights about trends from across its global branches, involving thousands of cameras, VMSs, and data centers, among others. The needs of an organization processing twelve hours of video locally will be vastly different than those that must centralize data from thousands of sources around the clock. Your video analytics provider can work with you to support your deployment requirements and advise which supporting technologies and hardware can drive your individualized needs.

The Top Factors to Consider when Deploying VCA

While the aforementioned practical considerations will arise for any business implementing video analysis, the needs of every organization are unique, and, when considering any new solution, it is critical to understand:

  • What enabling technologies are required to ensure the success of your integration
  • How the new technology will complement and enhance your existing investments
  • Whether the new solution can scale as your business requirements evolve, and how flexibly it can do so

When it comes to video content analytics solutions, having a comprehensive and extensible solution with broad analytic capabilities will enable you to evolve as a business and streamline decision-making based on actionable intelligence. However, the key first step in revolutionizing your business based on video is understanding what is required to facilitate this transformation.