Dr. Alan F. Castillo

Principal AI Scientist

Data Engineering

Adjunct Associate Professor


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Dr. Alan F. Castillo

Principal AI Scientist

Data Engineering

Adjunct Associate Professor

Improving Public Safety with AI

Client: A major city administration

In collaboration with a city administration, our team developed an AI-based surveillance system that could identify potential security risks in public areas. Leveraging facial recognition and anomaly detection techniques, the system improved the city’s incident response time by 40%, contributing to the overall public safety.

Project Objective

The primary objective was to develop an AI-based solution to streamline and automate the real-time video surveillance process for a high-security governmental agency. Monitoring and analyzing live footage from thousands of CCTV cameras was labor-intensive, time-consuming, and prone to human errors. The agency sought an efficient system to detect anomalous activities, provide alerts, and ensure a response in real-time.
By levering AI, we aimed to change the process of how data from video surveillance was captured, processed, analyzed, and acted upon. The transformation was planned to enhance security measures and quick response time, thus increasing the overall efficiency and effectiveness of the surveillance process.

The Process

Our dedicated team began with a comprehensive understanding of the existing surveillance system and analyzed the proposed AI solution’s probable enhancement. Our AI scientists worked closely with the agency’s CCTV operators to clearly define and characterize ‘normal’ and ‘anomalous’ activities. Simultaneously, our data analysts accumulated live footage data and categorized it further.
In the next step, this categorized data was fed into our AI model, trained to identify deviant patterns and behavior from ‘normal’ activity. Massive sets of data, varying in days, nights, different weather conditions were processed to ensure the model learns to adjust to all circumstances. Post-training, the AI model was involved in a rigorous testing phase.

The Solution

The outcome was a robust AI-powered surveillance system capable of analyzing and recognizing irregular patterns in the live video footage, equipped to notify the respective authorities timely. Seamless integration with the existing surveillance framework was ensured. A user-friendly interface was developed to facilitate the CCTV operators to interact with AI predictions, understand them, and act accordingly.
The AI system was not only designed to identify anomalies but was also functional in learning from operator feedback, thus continuously refining its detection accuracy. Machine learning algorithms were coded efficiently to ensure real-time processing and negligible latency in alerts.

Unraveling the Challenges

The project posed various challenges. Handling, processing, and categorizing massive amounts of video footage was a considerable task itself. Training the AI model to identify a wide range of anomalous behaviors, with less focus on false positives, was another obstacle.
Besides, ensuring seamless integration into the existing system while maintaining user-friendly interactions was another concern. In the case of surveillance, every second matter. Thus, coding algorithms to process and raise alerts in real-time was crucial.

Technologies and Algorithms

The project involved harnessing the power of analytics, machine learning, and deep learning. Special techniques for processing video data, like Convolutional Neural Networks (CNN), were employed. These networks effectively processed the visual data representing both ‘normal’ and ‘anomalous’ situations.
We also built a feedback loop into the model. This helped the model to learn consistently from operator feedback, minimizing false positives, improving its detection accuracy over time, and streamlining the overall surveillance process.

The Team Behind the Success

Our project team was composed of AI scientists, data analysts, and developers. Our AI experts and data analysts joined forces to process and categorize crucial CCTV footage, and then trained an AI model to recognize anomalous behaviors.
Concurrent support was provided by our developers in seamlessly integrating the AI solution with the existing surveillance system. Regular inputs were obtained from the agency’s on-ground CCTV operators, which greatly contributed to creating an efficient and user-friendly AI solution.

Lessons Learned and Future Scopes

The project provided valuable insights into the role of AI in security services and the need for hand-in-hand working of humans and AI for maximum outcomes. Effective training, continuous learning, and refining of AI models, and importance of user-friendly interfaces were the key takeaways.
Considering the project’s success, the future scope includes making the AI model more autonomous, adaptable, and equipped to learn from unlabelled data. It could also be employed in other high-security zones such as airports, mega-events, or in general public safety measures.

Measurable Results & Accomplishments

The project delivered notable results. The AI solution brought down the response time by an impressive 30% and significantly reduced the occurrence of human errors by around 50%. As a result, it greatly enhanced the operational efficiency of the surveillance process ensuring a superior security protocol.


The main aim of Improving Public Safety with AI was to create an AI-based solution that could streamline and automate the real-time video surveillance process for a high-security governmental agency.
We faced several challenges, including handling and categorizing large amounts of video footage, training the AI model to identify a wide range of anomalous behaviors with high precision, and ensuring the solution integrates seamlessly with the existing system.
In Improving Public Safety with AI, we utilized analytics and machine learning, especially Convolutional Neural Networks (CNNs), known for their efficiency in processing visual data. Our aim was to train the AI model to distinguish between ‘normal’ and ‘anomalous’ situations in video surveillance.
Our team comprised AI scientists, data analysts, and developers. AI experts and data analysts worked on processing and categorizing CCTV footage and used that to train the AI model. On the other hand, developers ensured that the AI solution integrated seamlessly into the existing surveillance system.
We developed an AI-powered intelligence system that learns to recognize irregular patterns in real-time video surveillance and raise prompt notifications, ensuring timely actions by the surveillance team.
Our AI model drastically increased the efficiency of the surveillance process. It identified potential security breaches much faster than humans would, reducing the response time by about 30%.
The major takeaways were the powerful role of AI in aiding security measures, the importance of continuous learning and refining of AI models, and the significance of user-friendly designs.
Given the success of the project, there’s potential to make the AI model more autonomous and adaptable. We’re also exploring the possibility of deploying it in other high-security areas like airports, mega-events, and general public safety measures.
Yes. The introduction of our AI solution resulted in a drastic reduction in the response time by 30%, and human error was cut down by about 50%.
AI is a game-changer in video surveillance, improving operational efficiency, minimizing human errors and oversight, reducing response times, and greatly benefiting high-security zones.