It’s no secret that the world of technology is moving closer and closer towards bigger and better data, but few people are aware of how edge-based video analytics can play a role in this. With the rise of machine learning and artificial intelligence, businesses are now able to better utilize big data by harnessing it on the edge. Edge-based video analytics is a powerful tool that allows businesses to capitalize on this data and gain insights on their customers, their processes, and their environment. In this article, we’ll take a look at how edge-based video analytics is changing the way businesses think about big data and how it provides the tools needed to create a competitive advantage.
1. What is Edge-based Video Analytics?
Keeping track of data from video sources and systems can often be a difficult task due to its sheer size and complexity. Edge-based video analytics is a powerful tool for harnessing big data on the edge and provides an efficient way to identify and capture information from videos.
Here are some key benefits of edge-based video analytics:
- Cost savings: Edge-based video analytics creates cost savings by reducing system costs associated with data storage, processing, and transmission.
- Real-time data collection: Edge-based video analytics enables real-time data collection, so users can analyze data on the spot and make decisions quickly.
- Scalability: Edge-based video analytics is highly scalable, so it can be used to monitor a single camera, or a large network of cameras.
- Improved decision making: Edge-based video analytics helps in providing better insights and informed decisions in business operations and critical events.
Edge-based video analytics is a powerful tool for businesses and organizations to monitor areas such as retail stores, airports, and public spaces. It can be used for tracking customer behaviour, monitoring traffic flow, analysis of security, and other activities such as identifying objects or people. By leveraging advanced analytics and algorithms, edge-based video analytics helps organizations to get insights out of the massive volume of data coming from videos.
2. Traditional Big Data Analytics vs Edge-based Video Analytics
Todays demand for data-driven decision making and analytics can be overwhelming for organizations. Traditionally, businesses relied solely on big data analytics techniques to make sense of the data flow they received from their customers. But now, with the rise of edge-based video analytics, organizations are even more empowered to glean insights from their data.
Edge-based video analytics allow organizations to analyze their data on the edge, meaning they can detect patterns and detect trends without waiting for the data to “come back to their core processing system”. This reduces latency and provides almost instantaneous insights.
What makes edge-based video analytics so powerful is that it is able to capture more data than ever before. It not only captures the usual metrics like temperature and pressure, but it can also detect human presence, motion, and sound. This gives organizations real-time insights into the effectiveness of their campaigns, and provides valuable feedback as to how to improve their strategies.
Edge-based video analytics also help organizations detect abnormalities in their data and flag any problems or opportunities to act quickly. This helps organizations optimize their operations and improve their bottom line.
- Advantages of Edge-based Video Analytics:
- Reduced latency and instant insights.
- Detects abnormalities in data and helps optimize operations.
- Captures more data than traditional analytics.
- Provides real-time insights into effectiveness of campaigns.
Edge-based video analytics is a powerful tool that can help organizations harness the power of big data to its fullest potential. By leveraging the technology, organizations can access real-time insights and make faster, more accurate decisions that can help improve their operations and increase their revenue.
3. Benefits of Edge-based Video Analytics
1. Cost Reduction
Edge-based video analytics takes the traditional approach of video surveillance and cuts costs by deploying physical infrastructure closer to the point of video capture. This means less overhead for data storage, running of applications, and other IT related tasks, freeing up budget for other tasks.
2. Faster Insights
Analytical insights generated from edge-based analytics can yield results faster than traditional methods, as the analytics can be run directly at the edge of the network. This means better security for businesses, as they can identify and act on potential security threats quickly and effectively.
3. Improved Scalability
Edge-based analytics provides greater scalability than traditional surveillance systems. Instead of relying solely on a central server, data is processed across hundreds of devices on the edge, which means that each device can perform the same analysis at the same time and scale in accordance with demand.
4. More Comfort to the End-user
With edge-based analytics, the user’s data remains secure as the data never leaves the edge device. This is a crucial factor for companies handling sensitive data, such as banks, retail stores, and hospitals, as they can guarantee their customers enhanced levels of privacy and security.
5. More accurate Streams
Edge-based video analytics can be combined with facial recognition software and other technologies to improve accuracy and efficiency. This makes it ideal for use in traffic control, public surveillance, and security systems. Not only is the data more secure, but the data that is received is more accurate, which increases safety in a variety of industries.
4. Challenges Faced with Edge-based Video Analytics
- Long Distance Factors: Edge-based video analytics has its share of bottlenecks when it comes to sending video data over long distances. With traditional analytics methods, you had to send your analytics data to the cloud where it would be processed and analyzed, which takes up high amounts of bandwidth as well as time. With edge-based video analytics, the video data is processed and analyzed closer to the source, or “on the edge,” which reduces latency, protects confidential data, and increases efficiency.
- Fragmentation of Data: There are several formats and types of data that can be processed and analyzed for edge-based video analytics. This can range from video frames to audio, text, and sensor data – all of which need to be combined and analyzed in an effective and reliable manner. This can be difficult when the data is fragmented, so it needs to be pieced together in order to get an accurate and comprehensive picture.
- Dynamic Environments: Basically, the more dynamic an environment is, the more difficult it is to accurately analyze the data from the edge. This is because the analytics need to consider the context of the data in order to accurately interpret it, a task which can be particularly challenging when the environment is constantly changing.
- Varying Conditions: Not only are edge-based video analytics implemented in dynamic environments, but they also need to be able to accommodate varying conditions such as low-light, noise, motion, and shadows. This can be incredibly difficult to do accurately on the edge and requires advanced algorithms and a deep understanding of the environment to successfully analyze the data.
Edge-based video analytics provides several advantages when it comes to quickly and accurately analyzing large amounts of data. However, there are some challenges that need to be addressed in order to make the most out of the technology. These can range from long distance factors, fragmentation of data, dynamic environments, to varying conditions. All of these factors need to be taken into consideration to make sure that the analytics are as accurate and efficient as possible. By understanding the challenges and making the right adjustments, businesses can ensure that they are able to successfully harness the power of big data and make informed decisions with the help of edge-based video analytics.
5. Real-Life Applications of Edge-based Video Analytics
The ability to analyze data from video sources in real-time is a powerful and ever-evolving tool. Utilizing advances in technology, many organisations can now leverage Edge-Based Video Analytics (EBVA) to monitor and respond to real-world events quickly. Here’s an overview of 5 real-world uses of EBVA and how they can provide invaluable insights.
1. Smart Surveillance
EBVA has enabled the use of facial recognition for smart surveillance. By recognizing specific people in a given environment, companies can more effectively track their whereabouts, ensuring their safety, and/or tracking potential threats. It’s an effective solution for eliminating false alarms, something that traditional motion detection systems have difficulties doing.
2. Fraud Detection
Fraud is an inevitable part of running a business. Analytics have enabled businesses to spot discrepancies in routine operations like suspicious transactions or people, and quickly intervene. EBVA creates a secure network for businesses to detect and respond to fraud, while ensuring the security of their data.
3. Automated Inventory Management
Using EBVA, automated inventory management can be performed in real-time. This helps create an efficient and accurate system for businesses, ensuring that items are never over-stocked or under-stocked. It can also provide organisations with feedback on which products are selling well, allowing them to pivot seamlessly.
4. Crowd Management
CCTV cameras and simple crowd counting systems don’t always provide the info businesses need to ensure safe crowds. EBVA can provide detailed information on the makeup of crowds, including gender, age, and clothing. By gathering this information in real-time, organisations can more effectively monitor and prevent unsafe conditions.
5. Smart Retail Studies
Retail businesses can harness the power of EBVA to gain valuable insights into their shoppers’ behaviours. The technology can track how many people enter a store, how long they stay for, and where they spend the most of their time. This offers crucial insights into how customers interact with stores, allowing organisations to tweak layouts and patterns to increase sales.
With the right technology, Edge-Based Video Analytics can provide invaluable insights for businesses of all sizes. By leveraging artificial intelligence and real-time analytics, organisations can get smarter and faster. As the technology progresses, we’ll likely see more applications of EBVA in the market. These 5 real-life applications demonstrate the potential of the technology and how it can be used to create more efficient and secure systems for businesses.
6. Recommendations for Implementing Edge-based Video Analytics
To unlock the potential of edge-based video analytics, organizations must consider deploying purpose-built analytics applications. Such applications can harness real-time analytics to allow for more effective responses to incidents, disputes, or events that are occurring in real-time. It is important to use an analytics platform that is reliable and secure, capable of ingesting large amounts of data, and is able to bridge the gap between real-time and historical video analytics.
1. Scalable Platform: Organizations must ensure that the selected analytics platform is scalable enough to support the required video streams, features, and insights from the edge. The platform must be capable of supporting a high volume of data streams that can be used to perform analytics on the edge and provide actionable insights in near-real-time. This platform must also provide adequate resources and tools to process the data quickly and accurately.
2. Process Monitoring & Visualization: To get the most out of edge-based video analytics, organizations should deploy analytics solutions that enable process monitoring and visualization. Such solutions should be able to track patterns and dependencies of events captured by the video feeds, thus enabling better understanding of an incident or situation. Visualization solutions present the data in an easy-to-understand graphical format, enabling users to take quick and informed decisions.
3. Alerts & Notifications: To ensure that no critical incident goes unnoticed, organizations must deploy automated alert and notification systems. Such systems can raise an alarm about an event or incident taking place, and can be set up for both real-time and historical video feeds. Automated alerts and notifications are invaluable for providing quick responses to incidents or events.
4. Data Security: When dealing with large amounts of data, it is important to ensure that the selected analytics platform has robust security measures. This will provide assurance that the data collected and processed through the platform does not get into the wrong hands. Organizations should also ensure that the video streams are encrypted and transmitted securely.
5. High Availability: As edge-based video analytics requires continuous and reliable data flows to provide insights and alerts, organizations should also deploy high availability solutions. Such solutions provide uninterrupted access to the cloud for data analysis and ensure that the analytics platform is always available and working as expected.
6. Human Intervention: Even with the best analytics applications in place, organizations should deploy trained and experienced personnel to monitor the analytics platform and interpret the data and insights generated. Human intervention is important to validate and verify the accuracy of the analytics and insights and to provide an additional layer of security.
Q&A
What is Edge-based Video Analytics?
Edge-based Video Analytics is a technology that uses computer vision tools to analyze video data sources, such as CCTV cameras, in real time.
What are the benefits of Edge-based Video Analytics?
Edge-based Video Analytics allows for the collection and analysis of large amounts of data from multiple sources quickly and efficiently. This enables businesses to identify patterns and extract insights from video footage in almost real time.
What data can Edge-based Video Analytics capture?
Edge-based Video Analytics can capture data relating to objects, movements, and events. This may include facial recognition, license plate tracking, people counting, object tracking, and more.
Who can use Edge-based Video Analytics?
Edge-based Video Analytics can be used by businesses, including retailers, airports, governments, and other organizations, to gain insights from video data sources.
How is Edge-based Video Analytics different to traditional video analytics?
Unlike traditional video analytics, Edge-based Video Analytics harnesses data on the edge of the network, meaning the data is processed and analysed locally, rather than in the cloud. This allows for faster, more efficient data collection and analysis.
What type of insights does Edge-based Video Analytics offer?
Edge-based Video Analytics can offer insights into customer behavior and trends, stock levels, traffic flow, security incidents, and more.
How secure is Edge-based Video Analytics?
Edge-based Video Analytics is highly secure, with data collected on the edge of the network being encrypted and stored securely.
What kind of results can Edge-based Video Analytics provide?
Edge-based Video Analytics can provide detailed reports, including statistics, visualizations, and insights into customer behavior, traffic patterns, and more.
What are the most important considerations when implementing Edge-based Video Analytics?
It is important to consider any privacy regulations that may be applicable, as well as the accuracy of the technology and the data it produces. It is also important to ensure that the data is stored and processed securely. Edge- based Video Analytics is reducing the burden of surveillance and security system operators and allowing for quicker retrieval and analysis of data. With these powerful tools, organizations are now able to benefit from higher security, less storage costs, and the ability to leverage Big Data on the Edge. The future of Edge- based Video Analytics looks bright, and we look forward to seeing how this technology changes the landscape of our modern world.