Vehicle area segementation using grid-based feature values

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Vehicle area segementation using grid-based feature values

Baek, Nakhoon / Kim, Ku-Jin / Hong, Manpyo

The authors present a vehicle segmentation method for still images captured from outdoor CCD cameras. The preprocessing process partitions the background images into a set of two-dimensional grids, and then calculates the statistical feature values of the edges in each grid. For a given vehicle image, the feature values of each grid are compared to the statistical values of the background images to finally decide whether the grid belongs to the vehicle area or not. To find the optimal rectangular grid area containing the vehicle, a dynamic programming technique is used. Based on the statistics analysis and the global search technique, this method is more systematic compared to the previous heuristic methods, and achieves high reliability against noises, shadows, illumination changes, and camera tremors. A prototype implementation performs vehicle segmentation in average of 0.150 second, for each of 1280 x 960 vehicle images. It shows 97.03 % of successful cases from 270 images with various kinds of noises.