What is Retail Video Analytics?
Video analytics uses mathematical algorithms that automatically monitor, analyze and manage large volumes of video data (including metadata). It digitally analyzes video inputs; transforming them into intelligent data that help in making decisions.
The need for video analytics is so massive that humans alone simply cannot audit the volume of video footage created by surveillance and other cameras. As these video sources are scalable to hundreds or thousands, they need to be backed and augmented by technology. And that’s a great thing. Computers can process a huge amount of information in real-time without fatigue. The next step is to analyze the data effectively and deliver valuable insights which requires a level of intelligence typically defined as artificial intelligence (AI)
Let’s take a step back and think about what’s making all this innovation possible before we discuss how analytics are being used in retail.
THINK about storage and image processing
We have seen a considerable enhancement in image resolution, dynamic range, frame rates and light sensitivity with each generation of a video camera by which we’re able to capture more details than before. Simultaneously, we’ve seen advancements in compression algorithms to decrease bandwidth utilization and capacity. Hence, video analytics helps structure and sense the massive amount of data so that we can zero in on the most relevant footage while directing cameras to record and further reduce bandwidth and storage needs.
THINK about artificial intelligence (AI) and deep learning
A camera is no longer just a lens, sensor and CPU. Today, we’re witnessing a fusion between camera vision with deep learning technologies which are enabling video analytics to become more accurate in extracting and classifying metadata and transforming video into usable intelligence. When you input these analytical data into AI engines, it will not only provide us with intelligent information but serves as a basis for predicting future trends and behaviors which helps us enhance our own decision-making over time.
THINK about intelligence and metadata
The unstructured video data uses metadata to give context to visual images and makes them easy to find, understand and use in helping users quickly glean actionable intelligence from what they see, whether it is classifying objects or attributes like color, speed, and direction. You can also identify a person’s mood (happiness, sadness and anger) with analytics that use sentiments. This ability to aggregate and parse video data in numerous ways makes video analytics a super tool with endless possibilities.
Let’s look at our compiled list of 10 ways on video analytics use cases currently being used in retail operations
Face Recognition – From security to advertisements, a multitude of applications of face recognition are being used in retail. From sending customized advertisements to identified shoppers to identification of gender, age, customer count, capturing glance time on products, the applications are endless.
Retail Analytics – With changing customer needs, retailers are implementing in-store analytic platforms. These platforms provide queue analysis, people counting, heat maps, etc. so that the system tracks where people are spending time and which products or services they come into contact with.
Checkout-Free Stores – This new application, driven by an array of cameras and sensors, tracks customers’ movements and items taken from the shelves, and processes the data using computer vision and machine learning to determine who to bill for what.
Customer Intelligence – Knowing how many customers enter and exit stores is valuable information. This information can be integrated with POS/checkout systems to generate useful insights.
Ad Metrics/Dwell Analysis – An essential retail metric for analyzing shoppers’ behavior and measuring customer dwell time with products and promotions, helps improve and increase sales.
Retail Security – Retail environments demand special attention for both indoor & outdoor requirements that span security, operational concerns & safety. Transforming any camera into an alarm sensor with state-of-the-art adaptive video analytics platforms, provides push notifications to a central monitoring station that can help reduce costs and improve operations.
Queue Management – Algorithms here manage your retail floor with in-depth analytics that monitor POS stations and other areas. At the same time, data can trigger alerts that notify any delay in service to allow better service for your customers and reduce unnecessary queues.
Shelf Replenishment Alerts - Shelves have become more than just a surface for storing and displaying objects. Technology is enabling interactions between shoppers, and the racks they’re standing in front of with 3D cameras, proximity sensors, RFID and microphones, etc.
Omnichannel Experience – Retails are adopting omnichannel strategy as limited presence typically derails both the user experience and the bottom. Learnings from offline video analytics can be used to improve the online shopping experience.
Control Fraud and Shrinkage – Inventory shrinkage can be minimized with the use of video surveillance installed in storage areas. The data captured can be analyzed to get insights about normal operating hours, average time spent in the inventory, employees handling the inventory, and other details. In these ways, while monitoring inventory levels, they can get alerts in real-time and prevent losses before they occur.
Wrapping it up
Artificial intelligence (AI) capabilities can equip retailers with greater decision-making ability. Large volumes of data in various formats across locations can be analyzed and one can learn from patterns, and respond in real-time.
The digital transformation journey has just started by combining the success formula of hyper-personal branding and immersive customer experience through technology. There is a dramatic technological shift like digital racks for fashion products, virtual trial rooms, robotic assistance, customer support via chatbots, etc. that will be experienced by retailers in the coming year. Weighing up both risks and opportunities, we will continue to share and implement the potentials of new technologies working towards growing digitally-connected futures.
Pathpartner helps build custom video analytics solution for all your retail needs. Consult our experts to harness the true potential of AI for your retail operations.
- RTLS in Retail – Why It’s the Need of the Hour
- A brief analysis of Indoor Positioning market
- Developing Convolutional Neural Networks for Face Recognition
- Compression Techniques for Computer Vision Application
- PathPartner’s Face recognition Solution
- RTLS in Retail
- Imaging Algorithms
- Camera Modules
- Connected Cameras