Learning-Based Vehicle Detection Using Up-Scaling Schemes and Predictive Frame Pipeline Structures
This paper aims at detecting preceding vehicles in a variety of distance. A sub-region up-scaling scheme significantly raises far distance detection capability. Three frame pipeline structures involving object predictors are explored to further enhance accuracy and efficiency. It claims a 140-meter detecting distance along proposed methodology. 97.1% detection rate with 4.2% false alarm rate is achieved. At last, the benchmark of several learning-based vehicle detection approaches is provided.