Smart City Surveillance

Case Study 1: Antioch Homicide Investigation (Veritone AI)

Rob Gerber, a former detective with the Antioch Police Department in Northern California, provided a compelling account of how AI tools, specifically Veritone, could have dramatically reduced the investigation timeline for a high-profile homicide case. During the manual investigation, despite having initial surveillance video and cell phone data, a crucial connection—the suspect’s van being advertised for sale on social media—was missed for over a year. Gerber posits that an AI-driven solution could have immediately correlated this disparate data. AI can efficiently sift through hundreds of hours of surveillance video and immense volumes of cell phone data in minutes, identifying relevant evidence and making critical connections that are often missed or significantly delayed through manual review. This capability can save months of investigation time and hundreds of thousands of dollars in operational costs.

The Antioch case study provides concrete, quantifiable benefits beyond mere efficiency, revealing the tangible return on investment of speed in criminal investigations. The potential savings of “10-plus months of investigation” and “probably a couple hundred thousand dollars of the wiretap investigation” go beyond simply solving crimes to demonstrating a clear, measurable economic return on investment. Furthermore, a faster resolution translates to quicker justice and closure for victims’ families, which represents a profound societal value that extends far beyond monetary figures. This aspect is crucial for public safety decision-makers who must justify AI investments within budget constraints.

Case Study 2: Citilog AID System for Traffic Incident Detection (Maryland, NY Bridge Authority)

Citilog’s Automatic Incident Detection (AID) system employs advanced AI and deep learning to detect a comprehensive range of traffic incidents, including wrong-way vehicles, pedestrians on roadways, debris, smoke in tunnels, and traffic congestion. A key advantage of AID is its ability to achieve up to 90% fewer false alarms compared to traditional systems. The Maryland Transportation Authority currently utilizes AID to detect and respond to incidents within seconds, significantly improving response times. Similarly, the New York State Bridge Authority is actively upgrading its systems with AID to enhance operational efficiency and accelerate incident response. The system delivers significantly faster response times (reducing response from minutes to seconds), actively prevents secondary accidents, optimizes traffic flow, and ensures efficient resource allocation by allowing operators to focus solely on genuine incidents.

Case Study 3: NASA and AI-Powered Wildfire Detection in California

NASA, in collaboration with AI-powered wildfire detection systems, processes extensive satellite imagery and meteorological data to identify wildfire hotspots and predict fire spread patterns using sophisticated machine learning models. This joint effort has been credited with helping to predict and prevent over 50 wildfires in California. Furthermore, AI-equipped cameras are increasingly deployed as “digital fire lookouts” in wildfire-prone regions such as California and Australia, continuously analyzing visual data to identify smoke patterns, heat anomalies, and early fire ignition points, alerting emergency services before wildfires become uncontrollable. These systems facilitate the proactive identification of high-risk zones, enable more effective resource allocation, and guide aerial fire suppression efforts, thereby reducing human error and significantly enhancing fire containment strategies.

Case Study 4: Texas Department of Insurance – Video Analytics in Construction Safety

The Texas Department of Insurance prominently features how video analytics (VA), powered by AI and machine learning, analyzes security camera footage on construction sites in real-time. VA is designed to detect a wide array of safety risks, including workers being too close to edges without proper safety gear (preventing falls), the presence of loose tools or falling materials (stopping struck-by incidents), workers near live wires without appropriate protective gear (guarding against electrocution), unsafe lifting techniques (reducing strains), and signs of structural instability in trenches and walls (preventing collapses). The system also monitors Personal Protective Equipment (PPE) compliance, detects potential vehicle accidents, and tracks unauthorized access to restricted areas. This technology enables the proactive prediction and prevention of injuries and fatalities within the inherently high-hazard construction industry. By spotting risks in real-time, it empowers supervisors to take immediate corrective actions before incidents occur, significantly enhancing workplace safety.

The construction safety example from the Texas Department of Insurance provides a compelling case for AI’s capacity to continuously and consistently enforce safety protocols. Human observation, particularly in dynamic and complex environments, can be prone to errors due to fatigue, distraction, or the sheer volume of activity. AI, however, can relentlessly monitor PPE compliance, proximity to hazardous machinery, and unsafe behaviors 24/7. This shifts the safety paradigm from reactive accident investigation to proactive, granular-level risk mitigation. The implication is a significant and measurable reduction in workplace accidents and a cultural shift towards continuous, data-driven safety enforcement.