Improving mobility and public safety is now a critical and urgent matter for multiple industries. The use of new technologies, including artificial intelligence, has become increasingly important for public safety agencies.


Since the GDPR law came into effect in Europe, privacy has become a big concern in many domains. There are numerous user cases, such as mapping, parking, and indoor monitoring where personal data like faces, license plates, vehicles is captured in images and videos. AI can be utilized as an automated and scalable solution for data anonymization. It will significantly increase accuracy and save cost by avoiding manual work and human errors.


Crowd management and monitoring is crucial for maintaining public safety, especially in the context of dire situations like the COVID-19 pandemic. The current use of CCTV has many drawbacks, such as limited area coverage, low accuracy, high redundancy, and the growing labor cost from constant monitoring by the operators. Computer vision and machine learning are the optimal choice to overcome these issues and minimize human involvement. This technology can be applied to crowd monitoring and associated tasks such as counting, density estimation, tracking, scene understanding, localization and behavior detection.


To maintain sustainable road safety, traffic enforcement is widely used by authorities to reduce high-risk road user behavior. The growing volume of traffic poses a big challenge for the authorities. It is increasingly difficult to enforce road safety in terms of human interference, cost, accuracy, data retrieval etc. Using computer vision, traffic violations, such as unauthorized vehicle passing or helmet-free motorcyclists, can be monitored and automatically detected. Combined with ticketing and reporting applications, AI can enable a smart and efficient traffic violation detection and monitoring system.


Today, traffic has come progressively complex, with different types of road users intermingling such as vehicles, pedestrians, bicyclists, and scooters. This complexity and variability create many challenges for city planners since traditional methodologies cannot predict modern traffic. AI can be used to improve intelligent traffic systems by managing and analyzing the massive amount of traffic data. It can be used in areas like vehicle control, traffic prediction, and road safety, to optimize efficiency in complex traffic scenarios.

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