Martin E. Hoover

Vehicle speed estimation using a monocular camera

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.

Vehicle occupancy detection camera position optimization using design of experiments and standard image references

Camera positioning and orientation is important to applications in domains such as transportation since the objects to be imaged vary greatly in shape and size. In a typical transportation application that requires capturing still images, inductive loops buried in the ground or laser trigger sensors are used when a vehicle reaches the image capture zone to trigger the image capture system. The camera in such a system is in a fixed position pointed at the roadway and at a fixed orientation. Thus the problem is to determine the optimal location and orientation of the camera when capturing images from a wide variety of vehicles. Methods from Design for Six Sigma, including identifying important parameters and noise sources and performing systematically designed experiments (DOE) can be used to determine an effective set of parameter settings for the camera position and orientation under these conditions. In the transportation application of high occupancy vehicle lane enforcement, the number of passengers in the vehicle is to be counted. Past work has described front seat vehicle occupant counting using a camera mounted on an overhead gantry looking through the front windshield in order to capture images of vehicle occupants. However, viewing rear seat passengers is more problematic due to obstructions including the vehicle body frame structures and seats. One approach is to view the rear seats through the side window. In this situation the problem of optimally positioning and orienting the camera to adequately capture the rear seats through the side window can be addressed through a designed experiment. In any automated traffic enforcement system it is necessary for humans to be able to review any automatically captured digital imagery in order to verify detected infractions. Thus for defining an output to be optimized for the designed experiment, a human defined standard image reference (SIR) was used to quantify the quality of the line-of-sight to the rear seats of the vehicle. The DOE-SIR method was exercised for determining the optimal camera position and orientation for viewing vehicle rear seats over a variety of vehicle types. The resulting camera geometry was used on public roadway image capture resulting in over 95% acceptable rear seat images for human viewing.