The challenges of protecting the dynamic environments of casino properties from theft, fraud, and criminal activity have perplexed the leaders of the gaming industry from the time the first dice were tossed, the first cards dealt, and the first slot machine handle pulled.
As much as casino properties have evolved into complete resort, entertainment, and dining experiences, the very nature of the business is a natural lure for criminal activity perpetrated by people who often go to extremes in their attempts to breach protective measures.
It is not just about the gaming floor. The scope of areas requiring surveillance has expanded significantly to include cash cages, ATMs, and ticket/cash redemption kiosks. Also, entrances, hospitality areas, restaurants and retail establishments, warehouses and loading docks, parking lots and garages, entertainment and sports venues, and transportation hubs.
The scope of protective measures has advanced far beyond the capabilities of teams of security personnel assisted only with technology that barely went beyond radio communication.
The first technological security systems were analog, which used video cameras to record footage on tape. By today’s standards it was very inadequate in addition to being inefficient. A significant monetary investment in man-hours was required for personnel to not only monitor the live environment, but also track and review footage for investigative purposes.
The next step in surveillance evolution was closed-circuit television (CCTV), which required a network of cameras to transmit video signals to a central monitoring location. This technology was a step in the right direction of efficient security, but still relied heavily on human intervention.
This was followed by the introduction of internet protocol (IP) and digital cameras, which brought higher image quality, network connectivity, and remote accessibility. It was the start of a surveillance environment that also brought video management systems (VMS).
VMS software was designed to manage and analyze footage from IP cameras while making possible centralized control, storage, and retrieval of video data.
Which brings us to the present, sophisticated camera technology with capabilities never imagined, and the era of agentic AI behavior detection and its relationship to human behavior detection capabilities/limitations.
Human behavior detection is based upon observation, information dependent upon situational awareness, environmental baselines, and experience. It requires the ability to interpret and predict outcomes.
The variety of sophisticated criminal activity that is possible in a dynamic casino environment poses an insurmountable challenge to humans without the assistance of AI and video analytics.
Perpetrators themselves have vast knowledge of, and access to, high-tech capabilities to “beat the system”. They look for ways to make criminal activity undetectable, traditional “tells” unobservable, and suspect behaviors invisible.
AI systems have the capability of adapting to these new challenges by transforming what was once the passive recording of activities into proactive technology with the ability to analyze data and identify potential threats in real-time.
According to a Market Analysis Report from Grand View Research, AI integration into surveillance systems has significantly increased in recent years. The global AI in surveillance market was estimated at USD 196.63 billion in 2023, growing at a CAGR (Compound Annual Growth Rate) of 36.6 percent from 2024 to 2028.
Sambhavi Gopalakrishnan, Vice President, Strategy for the software development company VLink, Inc., with headquarters outside of Hartford, CT, observes in her blog that traditional surveillance systems rely heavily on human operators to monitor footage and identify potential threats. The approach was time consuming, prone to errors, and limited in its ability to detect subtle anomalies or patterns.
Gopalakrishnan acknowledges that AI has emerged as a powerful solution to the challenges by incorporating video analytics which can identify objects, people, and behaviors, enabling systems to detect potential threats, including suspicious activity, even abandoned objects, and alerting security personnel immediately,
In addition to video analytics using AI algorithms for facial recognition, object tracking, anomaly detections, and predictive analysis, Gopalakrishnan predicts that as AI technology advances, we can expect to see even more innovative applications in the surveillance field, including future developments in biometric authentication:
- Multi-modal biometrics combining multiple biometric modalities, such as facial recognition, iris recognition, and fingerprint recognition to enhance accuracy and security.
- Behavior-based authentication to analyze behavioral patterns such as typing speed or gait, to verify identity in addition to biometric features.
- Liveness detection to prevent spoofing attacks by using AI to detect whether a biometric sample is from a live person or a fake.
She also takes into consideration the high-volume traffic and challenging lighting conditions presented in casino environments for the real-time facial recognition power of AI in surveillance systems and the enhanced detection and tracking power of objects, people, vehicles, and animals in real time for more effective monitoring of crowded areas to identify suspicious behavior.
The vast amount of area on a casino property that includes parking lots, garages, storage facilities, and non-public access areas benefits from intelligent video analytics to identify anomalies such as unusual movement patterns or suspicious objects to detect potential threats and prevent incidents.
Gopalakrishnan also addresses predictive analytics whereby AI can analyze historical data to predict future events and identify potential risks. This enables proactive measures to prevent incidents and improve overall security.
Also, AI-powered systems can automatically detect incidents, such as theft, vandalism, or unauthorized access, and alert security personnel. This reduces response times and improves incident resolution.
The future of surveillance-powered AI analytics may hold many surprises according to Gopalakrishnan, including equipping drones with advanced AI capabilities for autonomous surveillance object detection, and tracking; developing robotic security guards to patrol areas, detect intruders, and respond to incidents; and the expansion of ongoing AI-powered security camera technology.
