How Artificial Intelligence Is Changing Video Analytics

Artificial Intelligence (AI) is redefining the capabilities and potential use cases for video analytics, expanding far beyond conventional security and surveillance applications. The integration of AI unlocks transformative new functionalities, from behavior analysis to predictive modeling and automated diagnostics. Video analytics powered by AI have the capacity to revolutionize applications across all sectors. 

Superior Insights Through Multimodal Analysis 

One significant advancement shaping analytics is the ability to associate video data with other sensor streams to gain enriched insights. By combining modalities like thermal cameras, LIDAR, IoT sensors, and more, AI can understand nuanced details about people, vehicles, and situations. 

In a corporate setting, for example, multimodal analysis could link video data with badge tap data to track employee movement patterns and ensure proper access control without excessive monitoring. For large events, video data could be combined with noise recognition to quickly pinpoint areas requiring fan guidance or intervention. A new generation of analytic solution capabilities will be based on emerging AI technologies, including: 

Automated anomaly detection — By establishing patterns of normal activity, AI-driven anomaly detection can identify events that deviate from the baseline as potentially suspicious. In airports, anomaly detection can enhance safety by recognizing unusual behavior that may indicate malicious intent. In retail, it can detect changes in customer traffic flows, transactions, and inventory abnormalities, including theft. 

Predictive modeling for risk mitigation — Predictive analytics takes this a step further by forecasting outcomes before they occur, enabling preemptive actions. By detecting precursors and warning signs, organizations can mitigate risks proactively. For public transportation agencies, predictive video analytics could forecast problems leading to system disruptions, allowing early interventions to maintain smooth operations. In elder care facilities, early indicators of health changes or accidents could trigger staff assistance. 

Natural Language interfaces and large language models — An intriguing aspect of evolving AI is the possibility for video analytic platforms to incorporate natural language capabilities. Conversational interfaces that utilize large language models, would allow users to query data and modify system parameters using voice and text, facilitating faster response. Rather than only displaying video, systems could provide auto-generated descriptions of events, objects, and behaviors in plain language. This would amplify human insight into monitored situations and speedup the responsiveness and even enable humans to be proactive rather than reactive. 

Integrated, Actionable Assistance 

Having a “human in the loop” is an integral part of these solutions. A predictive model may be used to send an alert to prompt human operators to review the video and decide if action is needed. The more data the system has available to learn from and the more feedback it gets from operators, the smarter the software gets at making predictions. 

While many may think the role of video analytics is to make decisions for humans, the reality is the opposite. Analytics simply analyze the vast streams of data, looking for anomalies that may indicate an activity or situation that is out of the ordinary. Video analytic solutions are most effective when combined with other security technologies, combining video, audio, and various complementary data for greater context and evaluation capabilities. 

Today's open platform, data-driven video systems can ingest, process, and present vast amounts of data. Video management software can connect video and audio devices, along with access control systems, IoT devices, and sensors for detecting everything from air quality to fire and smoke, to different types and locations of noises. 

With AI, the types of conditions detectable by video management software are nearly unlimited and can be custom-designed to meet an organization's specific needs. For example, in hospitals, using the latest-generation video management software allows various departments to monitor events in real-time and make informed, proactive decisions as they occur. These management platforms can also integrate with a facility's existing systems, such as Facility Management, EHR, HVAC, lighting, alarm, and intercom systems — creating a comprehensive, proactive approach to patient and staff safety. 

The Responsible Road Ahead 

We have entered the development point where rather than just passively recording data, video security is evolving into an intelligent system for proactive and informed responses. Integrating video data management with AI-powered analytics unlocks the ability to not only observe but to extract and act upon critical insights. As video analytics leverage machine learning and data associations to identify significant events and detect warning signs, powerful new possibilities arise for security teams. 

While it's exciting to think about the potential power of AI-driven capabilities and solutions, it's important to remember that AI is only as smart as the data it learns from. Leveraging responsible human oversight and industry expertise will maximize the value of AI solutions. As this new generation of analytics transforms security operations — transparency, accountability, and commitment to the public good will be vital pillars. With conscientious progress, AI-enabled video technology can usher in an era of enhanced safety, efficiency, and proactive threat response that will transform the industry.