Enhancing the Performance of Multi-Object Tracking in Traffic Stream Videos Through Initial Velocity and Frame-Skipping Strategies

Multi-object tracking in videos is an important task in various domains, such as traffic engineering and construction management. This paper proposes two methods, Grid Mean State and InCo-Skip, to improve multi-object tracking performance, particularly under frame-skipping scenarios. The study focuses on traffic flow counting, using YOLOv8 for vehicle tracking. Initial tests show that while car tracking remains accurate, motorcycles suffer a significant accuracy degradation when homogeneous frame skipping is applied. Grid Mean State addresses the issue by utilizing velocity vectors from earlier frames, and InCo-Skip provides an alternative skipping strategy to balance computational efficiency and accuracy. The combined methods show a substantial enhancement in counting accuracy, achieving up to 28.2% improvement for motorcycles under challenging conditions.