Vehicle Area Segmentation Using Grid-Based Feature Values

• Published in: Computer Analysis of Images and Patterns pp. 464 - 471
• Main Authors: Baek, Nakhoon, Kim, Ku-Jin, Hong, Manpyo
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Background Image; Computer science; control theory; systems; Exact sciences and technology; Grid Area; Horizontal Edge; Pattern recognition. Digital image processing. Computational geometry; Prototype Implementation; Vehicle Detection; Statistical analysis; Probabilistic approach; Image processing; Global analysis; Grid; Shadow; Vehicle dynamics; Search algorithm; Image segmentation; Image analysis; Luminance; CCD camera; Heuristic method; Fixed image; Illumination; Dynamic programming; Reliability; Pattern analysis
• ISBN: 9783540289692; 3540289690
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11556121_57

We present a vehicle segmentation method for still images captured from outdoor CCD cameras. Our 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, we compare its feature values of each grid 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, we use a dynamic programming technique. Based on the statistics analysis and the global search technique, our method is more systematic compared to the previous heuristic methods, and achieves high reliability against noises, shadows, illumination changes, and camera tremors. Our prototype implementation performs vehicle segmentation in average of 0.150 second, for each of 1280 × 960 vehicle images. It shows 97.03 % of successful cases from 270 images with various kinds of noises.