Lane Detection and Spatiotemporal Reconstruction Using the Macroblock Predictions Method
Edison A. Roxas, Ryan Rhay P. Vicerra, Gil Nonato C. Santos, Elmer P. Dadios, and Argel A. Bandala
Gokongwei College of Engineering, De La Salle University
2401 Taft Avenue, Manila 1004, Philippines
Detection and tracking of road lane markings offers several applications in intelligent transport systems (ITS). Although it is perceived as the simple task of isolating lanes on various types of roads, the accuracy of detection remains an issue. Several studies in recent literature have proposed solutions to this problem; however, none of these have used the method of macroblock (MB) prediction. This paper focuses on the type of MB applied for lane detection, tracking, and predictions, as well as the trade-off between the accuracy and complexity of implementing the system. This study makes the following contributions: (1) best MB for spatiotemporal lane detection and reconstruction; (2) best function approximation for lane predictions; and (3) best MB in terms of performance under different conditions.
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