In this paper, we describe a speed estimation method for individual vehicles using a monocular camera. The system includes the following: (1) object detection, which detects an object of interest based on a combination of motion detection and object classification and initializes tracking of the object if detected, (2) object tracking, which tracks the object over time based on template matching and reports its frame-to-frame displacement in pixels, (3) speed estimation, which estimates vehicle speed by converting pixel displacements to distances traveled along the road, (4) object height estimation, which estimates the distance from tracked point(s) of the object to the road plane, and (5) speed estimation with height-correction, which adjusts previously estimated vehicle speed based on estimated object and camera heights. We demonstrate the effectiveness of our algorithm on 30/60 fps videos of 300 vehicles travelling at speeds ranging from 30 to 60 mph. The 95-percentile speed estimation error was within ±3% when compared to a lidar-based reference instrument. Key contributions of our method include (1) tracking a specific set of feature points of a vehicle to ensure a consistent measure of speed, (2) a high accuracy camera calibration/characterization method, which does not interrupt regular traffic of the site, and (3) a license plate and camera height estimation method for improving accuracy of individual vehicle speed estimation. Additionally, we examine the impact of spatial resolution on accuracy of speed estimation and utilize that knowledge to improve computation efficiency. We also improve accuracy and efficiency of tracking over standard methods via dynamic update of templates and predictive local search.