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Enhanced Roadway Inventory Using a 2‐D Sign Video Image Recognition Algorithm
Author(s) -
Wu Jianping,
Tsai Yichang James
Publication year - 2006
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2006.00443.x
Subject(s) - algorithm , sign (mathematics) , computer science , pixel , artificial intelligence , image (mathematics) , computation , image processing , computer vision , mathematics , mathematical analysis
This article presents a two‐dimensional (2‐D) color, shape, and texture‐based stop sign recognition algorithm. The theoretical base of establishing a suitable 2‐D correlation coefficient threshold value that can both eliminate nonstop sign objects and minimize the possibility of false‐positive identifications is presented. Both the one‐dimensional (1‐D) algorithm, which has been developed by the authors, and the 2‐D algorithm provide a simple and feasible means to automatically extract stop signs from roadway inventory video images with a very competitive computation speed of less than 25 ms for processing a 400 × 300 pixel image. The 2‐D algorithm was able to correctly detect all of the images that were correctly detected by the 1‐D algorithm. In addition, the 2‐D algorithm could correctly detect two of the remaining five images that could not be correctly detected by the 1‐D algorithm. The type of images that can be correctly detected by the 2‐D algorithm but not the 1‐D algorithm is significant because there is a need to recognize the tilted stop signs. The 2‐D algorithm can detect stop signs tilted up to 35°. Similarly, the 2‐D algorithm can detect a stop sign with its surface being blocked up to 20%. The improved detectability, with only a marginally increased time in the image processing, suggests that the 2‐D algorithm is a better algorithm than the 1‐D algorithm for processing stop sign images from video logging of roadway inventory. This algorithm can be extended to recognize other signs, such as yield signs, with a slight modification.