Optimal Moving Windows for Real‐Time Road Image Processing

Abstract

In this paper, a “moving window” scheme for detecting lanes and obstacles from the images captured by a CCD camera in an automobile is proposed. Processing the input dynamic images in real time requires high performance hardware as well as efficient software. In order to relieve these requirements for detecting lanes and obstacles from the images in real time, a “moving window” scheme is proposed. The size of the moving window is optimally determined based upon road and automobile conditions to be big enough to detect the lanes and obstacles and to be small enough to process the detection procedure in real time. For each image frame, the “moving window” is newly defined and it is moved in a certain direction that is predicted by the Kalman filtering technique. By detecting the left and right lane marks of the driving lane, it becomes possible to search the obstacles within the lane. The obstacle can be verified through the correlation between the stochastic characteristics of the suspected obstacle and the actual obstacle in a database. The feasibility of the proposed algorithm is demonstrated through the simulated experiments of freeway driving. © 2003 Wiley Periodicals, Inc.

Applications of moving windows technique to autonomous vehicle navigation

“…It can cause the increase of unnecessary computation to apply preprocessing to total image in order to draw some necessary information. Therefore, the research on the decrease of computation time by designating a necessary part as a sub-block is being in progress [5][6][7][8][9]. This section explains edge detection based on image processing and central moment that is important when determining position of windows.…”

“…Again, this driving-supporting system is divided into two in view of a driver: an intelligence system and a detection warning system. The former copes with a given situation automatically when a driver cannot do so properly or saves some time during which a driver gets ready to do so [6]. The latter controls a driving situation autonomously under the supervision of a total intelligent traffic system such as a car navigation system.…”