Optimization of Image Processing in Video-based Traffic Monitoring
Keywords:Digital images, image processing, image quality, image color analysis
AbstractThe video-based traffic monitoring systems have been widely used. The system usually reads real time monitoring video and converts it into images for processing. However, such systems are often limited by image processing algorithms and don’t behavior as well as expected. We hereby propose optimization approaches for of image processing. As in image processing, getting a binary image is usually a fundamental step, we first present an adaptive thresholds approach for binary conversion. The approach takes into consideration the space information of the pixel and chooses thresholds by adaptively according to each pixel and its neighboring pixels. Then we introduce a three-dimension Gaussian filter, which has best quantity-time tradeoff, to remove noise in the image. Although widely used, background subtraction is limited by background refreshing. We propose a generative model approach that is based on Gaussian model and Gaussian distribution, to generate background so that we can update background at any time. We also add in moving objects shadow detection and removing mechanism in moving objects segmentation. In real world monitoring, removing disturbs from burst noise is a hard problem. We, taking advantage of the characteristic that most of the burst noise is sudden and short-term, put forward a burst noise eliminating algorithm that uses several continuous frames to wipe off such sudden noise. Particularly, we studied the characteristics of H, S and V component of reflection light band, and assert that removal of the reflection light band is to eliminate negative effect from high-energy reflection light. We use Gaussian model and Sobel operator to achieve reflection light band removing; we also as utilize Canny algorithm to wipe off edge corrosion. Finally we achieved an integrated optimized solution on traffic monitoring system by making a tradeoff between time and effect.
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