If you haven’t been keeping up with the latest in video analytics, the following statistics might give you pause. Since 2017, the global market for video inspection equipment has been steadily growing, with projections estimating it will reach $2.05 billion by 2027. Meanwhile, the video analytics market is expected to expand significantly, increasing from $4.55 billion in 2019 to $37.73 billion by 2030.
Why are these figures noteworthy? They highlight a significant shift from a hardware-centric approach to a software and data-intensive model in video intelligence.
The Current State of AI in Video Intelligence
Today’s live video streaming market is predominantly driven by high-quality, often proprietary camera systems used in surveillance and monitoring. Most of these systems rely on network video recording (NVR) technology, which enables network-connected edge devices to stream video data to local or cloud storage. This data is then available for further processing and analysis.
However, video data has lagged behind structured data in terms of processing and usability. Unlike structured data, which has been efficiently managed through APIs and sophisticated data pipelines, video data remains largely underutilized, especially in smaller businesses. Traditionally, video data has been used mainly for basic monitoring and surveillance, with some elementary AI applications for tasks like object and face detection.
The Promise of Generative AI in Video Intelligence
Recent advancements in generative AI offer new possibilities for video intelligence. Multimodal AI applications can generate text or video based on a series of prompts, combining text, images, and video inputs. Technologies from companies like OpenAI, Midjourney, and Runway have showcased the potential of multimodal AI, transforming creative use cases and enabling new forms of storytelling and visualization.
What remains unclear is how generative AI can enhance discrete video data for internal enterprise use. Consider the use case of video monitoring in gyms. Gym owners often use cameras to monitor equipment usage and investigate incidents. For example, in the event of a customer injury, recorded footage may be reviewed to determine if the equipment was used properly.
With AI-enabled video systems, you could proactively detect improper equipment usage before an incident occurs. Advanced AI cameras could alert you to potential issues and even initiate remediation workflows, such as displaying instructional videos to guide proper exercise techniques.
The Future of AI in Video Systems
If you find these scenarios ambitious, you’re not alone. Traditional camera systems capture high-quality footage but often restrict data access within proprietary systems. An open video system, however, allows for real-time data access and integration with business applications, making video data more actionable.
An AI-enabled open camera system can provide significant advantages. For example, it could summarize video feeds to quickly identify early signs of injuries and initiate corrective actions. In a generative AI scenario, you might even display simulated videos comparing proper and improper exercise forms in real-time, enhancing the customer experience and improving retention.
Staying Ahead in an AI-Driven World
Business owners and IT teams in sectors like gym management face the challenge of integrating advanced camera systems with outdated software. An open video management system can alleviate this issue by making video data accessible and usable across various business applications. By incorporating basic AI capabilities to summarize and analyze video data, you can maximize your investment in an open video platform and leverage AI’s potential to enhance your business operations.