Automatic Detection of Deficient Video Log Images Using a Histogram Equity Index and an Adaptive Gaussian Mixture Model

Video log images are often used by transportation agencies to manually or automatically extract roadway infrastructure information, including roadway geometry, signs, etc. Poor-quality images, especially those having illumination-related deficiencies caused by color corruption with a plain-like grayscale histogram, sun glare, or darkness problems, are unacceptable and need to be identified. Manually reviewing the tens of millions of video log images for quality control is labor intensive and time-consuming, so there is a need to develop automatic video log image quality control procedures. The contribution of this article is that it formulates a new problem of roadway video log image quality control and then proposes a reasonable solution to address this problem in the hope that it will motivate the development of new algorithms by other researchers. For the first time, an algorithm using a Histogram Equity Index (HEI) and an adaptive Gaussian Mixture Model is proposed to address the video log image quality issue by automatically detecting illumination-related deficiencies. The Alberta Department of Transportation provided 15,489 video log images to test the proposed algorithm. Test results show that the developed algorithm can detect illumination-related video log image deficiencies with a false positive rate of 4%, 3%, and 12%; a false negative rate of 15%, 17%, and 19% for plain-like color corruption, dark, and sun glare conditions, respectively; computation time is 0.1 second/image. The proposed algorithm could potentially be used to improve video log image data quality control.