Video data isn’t just for security anymore. It’s become a powerful tool for generating insights in industries like retail, transportation, manufacturing and healthcare. With video analytics, organizations and businesses can track foot traffic, spot safety risks and streamline operations.
One of the most strategic decisions you’ll face when setting up video analytics is where that data gets processed — at the edge or in the cloud. Each model has its own strengths, trade-offs and ideal use cases. Whichever video analytics solution you choose, your decision will directly impact performance, scalability and cost-efficiency.
What Is Edge Processing?
Edge computing in video analytics refers to the process of analyzing video right where it’s captured, on devices like IP cameras, on-site servers or other specialized edge appliances, instead of sending all the footage to a central cloud or data center. This approach cuts down on lag, saves bandwidth and enables quicker, real-time decisions.
Edge processing excels in industries where real-time data analysis is nonnegotiable, like finance and retail. However, due to its reliability and low latency, edge computing has become an attractive solution across all industries.
Some key benefits of edge processing include:
- Reduced bandwidth requirements: Since only metadata or critical events are sent to the cloud, edge computing significantly reduces the need to stream or store high-resolution videos over the network. This is especially ideal in environments with poor connectivity or high transmission costs.
- Lower latency for time-sensitive applications: Edge processing delivers immediate responses when you need to detect unauthorized access or activities. By eliminating the delay that comes with transmitting data to the cloud and back, edge systems support faster decision-making.
- Privacy and data sovereignty: With growing concerns about where and how sensitive data is stored, processing video locally offers stronger control over compliance with data regulation bodies.
- Functionality during network outages: Edge systems continue to operate even during disruptions in cloud connectivity, ensuring critical operations don’t go offline when the internet does.
Common Edge Processing Hardware Configurations
Edge computing isn’t one-size-fits-all. Depending on your analytics workload and type, you might be better suited to use:
- Smart cameras
- On-premise servers
- Edge artificial intelligence (AI) appliances
- Industrial sensors
- Computing machinery equipment
- Wearable devices
What Is Cloud Processing?
Cloud processing involves transmitting video data from edge devices to cloud platforms or remote data centers, where it’s stored, analyzed and managed at scale. This centralized model allows organizations to perform advanced analytics and streamline operations on a large scale.
Some of the top advantages of cloud processing are:
- Scalable computing resources: Cloud platforms offer virtually unlimited scalability. Whether managing a few cameras or thousands across multiple locations, cloud computing in video analytics lets you manage and analyze video data without major hardware overhauls.
- Centralized management: With cloud-based systems, you can manage devices, update software and monitor analytics from a single interface. This makes it easier to oversee different stations and maintain consistency across sites.
- Advanced analytics with larger AI models: The cloud’s computing power supports complex machine learning and deep learning models that are difficult to run locally. This opens the door to more sophisticated analytics, like behavior detection and crowd analysis.
- Easier updates and maintenance: Cloud environments are easier to maintain. Software patches, AI model updates and security enhancements can be deployed remotely without manual intervention at each site.
Deciding Between Edge vs. Cloud Computing
When planning your video analytics infrastructure, one of the most critical choices you’ll make is whether to process data at the edge or in the cloud. The best solution often depends on several factors, including technical and financial considerations.
Below are the key elements to keep in mind when deciding between edge versus cloud computing in analytics.
1. Bandwidth Availability and Cost
Video data is bandwidth-intensive. If your location has limited internet connectivity or expensive data plans, edge processing can significantly reduce costs by analyzing data locally and only transmitting what’s necessary.
Conversely, cloud processing requires high and consistent bandwidth, especially if you’re streaming high-definition video in real time.
2. Application Requirements and Response Time
If your use case involves real-time decision-making, like traffic control, edge computing would be the best option to ensure first processing. The closer the data is processed to the source, the faster your system can respond. Cloud computing offers more flexibility for less time-sensitive analysis.
3. Privacy Regulations and Data Compliance
Data privacy laws vary by industry and region. If you’re operating in a highly regulated environment, such as healthcare, finance or government, processing sensitive video data on-premises via edge computing can help you meet compliance standards more easily. With data-compliant video processors, you can have confidence in meeting strict regulatory standards across edge and cloud environments.
4. Budget Considerations
Edge solutions typically involve higher upfront capital expenditures for hardware deployment. Cloud-based analytics shift the cost model toward operational expenditures with monthly or annual subscriptions. Your decision may come down to how you prefer to allocate and manage your budget.
5. Deployment Scale and Growth
If your operation is rapidly growing or geographically dispersed, cloud computing offers the scalability and centralized control you need. Edge computing suits localized environments with consistent analytics needs, such as a single facility with no branches.
The Hybrid Approach
In many situations, choosing between edge and cloud processing isn’t straightforward. A hybrid approach, where video data is partially processed at the edge and partially in the cloud, offers a solution that balances performance, cost and functionality.
How Hybrid Video Analytics Works
With a hybrid approach, the first steps of video analysis happen right where the cameras are on local edge devices. These devices quickly sort through footage, tag essential moments and spot what matters most. This keeps things running fast and cuts down on the amount of internet bandwidth you need.
When you need more in-depth analysis, key video clips or data are sent to cloud systems. That’s where deeper learning happens, like spotting patterns over time. It’s also where everything comes together in one place for easy reporting and insights.
Use Cases That Benefit From Hybrid Deployments
While numerous industries can benefit from hybrid setups, here are some best-case scenarios:
- Traffic monitoring: In smart cities, license plate recognition and traffic light control can be handled at the edge, while long-term traffic flow analysis is performed in the cloud.
- Retail: Queue management and theft alerts can be processed locally, while customer behavior trends and sales analytics are analyzed in the cloud.
- Healthcare: Patient monitoring alerts can trigger instantly at the edge, while historical video data is archived securely in the cloud for compliance and research.
Considerations for Edge and Cloud Deployments
Choosing between edge, cloud or hybrid processing requires smart planning. Your hardware choices directly affect performance. Edge devices must be fast, efficient and durable, while cloud systems must support high data loads and advanced AI tasks. BCD’s validated appliances meet these demands with proven reliability.
Integration can be complex, but our solutions are designed for compatibility and ease of deployment, minimizing delays and technical issues. To stay ahead, future-proof your setup with scalable solutions and access to the latest tech. BCD’s original equipment manufacturer partnerships ensure you’re always equipped for uninterrupted operations.
Explore Video Analytics Solutions With BCD
Both edge and cloud processing have strengths that set them apart. Edge processing excels in speed, privacy and bandwidth savings, while cloud processing offers power, scale and central oversight. A hybrid model combines the best of both solutions.
At BCD, we offer specialized hardware for edge computing engineered to run AI-powered video analytics efficiently and reliably. Our edge appliances are validated under real-world conditions, ensuring your infrastructure is performance-tested before going into action. At the same time, our enterprise-grade analytics servers are designed to meet the high demands of cloud video analytics environments. These systems are cyber-hardened to protect sensitive video data from threats.
Our team offers the expertise, infrastructure and partnerships to help you build a high-performance and reliable video analytics system. We have you covered for security, retail, transport, city monitoring and more. Are you ready to enhance your video processing systems? Contact us today to learn more.
