Detection of vehicles from traffic scenes using fuzzy integrals
This paper presents an improved vehicle detection algorithm for traffic scene interpretation. In our previous high-level dynamic traffic scene interpretation systems (Dance et al., Picture Interpretation: A Symbolic Approach, World Scientific, New Jersey, 1995, Liu et al., IEEE Trans. Syst. Man Cybernet. Part B (2001) to appear), we used a simple background-subtraction procedure (referred to as Method 0) for vehicle detection, which, although adequate for well-defined image sequences, was not applicable to slightly displaced and noisy images. Our new method (referred to as Method 1) uses the Hough transform to extract the contour lines of the vehicles and morphological operations to reduce noise and improve the shape of the object regions. Finally, in the detection process we compute fuzzy integrals based on the evidence gathered. We have carried out extensive experiments. For all vehicles in the images, including the ones partially occluded and cut off at the image boundary, we were able to achieve a detection rate of 80% (Method 1) compared to 13% (Method 0). For the vehicles that are almost completely visible, the detection rate was 90%. In addition, for a set of 492 images, our new method reduces the number of false alarms from 427 (by Method 0) to only 9. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
fuzzy integrals vehicle detection hough transform morphological operations single-link clustering edge detection filtering confidence value