Moving object detection is vital in applications like traffic surveillance and collision warning systems. However, high training costs and slow detection challenge existing methods, hindering practicality and real-time performance. This chapter proposes cost-effective strategies to enhance detection speed without sacrificing accuracy, crucial for real-time applications. The chapter reviews moving object detection literature, emphasizing the need for reliable and efficient systems. It introduces novel techniques, including YOLOv6 processing and the Lucas–Kanade method for motion vectors. Results show reduced training costs, improved detection speed, and high accuracy. The chapter concludes by summarizing findings, highlighting the strategy's potential for precise moving object recognition in real-world traffic scenarios, with a focus on advancing the field for applications like driver assistance on urban highways.