Using Computer Vision to Boost Cities' Efficiency by Reallocating Police Resources

Joseph Rosenblum, a data scientist, explores the potential of using computer vision to improve city efficiency and reduce bias in traffic-related policing by transforming existing CCTV cameras into traffic enforcement tools. Given the financial constraints faced by municipalities due to the recessionary impacts of COVID-19, leveraging technology like CCTV cameras, which are already installed but underutilized, presents an opportunity to enhance public safety and optimize resources. By applying machine learning to footage from these cameras, Rosenblum aims to expand their capabilities beyond crime-solving to detecting traffic violations, thereby increasing their return on investment. Using the WebCamT dataset, which contains low-resolution footage from New York City, Rosenblum develops a custom model using YOLOv5 to identify vehicular violations, such as cars stopping in crosswalks. Despite challenges in model performance, he highlights the role of platforms like Roboflow in providing tools for image augmentation and model training, which have been instrumental in his ongoing project to refine and enhance the model's capabilities.