A Detection Algorithm for Rain-Affected Moving Objects

The degradation effects caused by raindrops are complex, which brings difficulties to image processing algorithms and objects detection. It is necessary to distinguish objects from the various fast moving raindrops or the blur effects caused by rain streaks when capturing videos in the rain. An algorithm is proposed to detect the moving objects in rain-affected videos which are obtained by the outdoor vision system. The chromatic property is used to structure the imaging model of raindrops and an imaging equation that includes the chromatic information of R, G and B channels is presented. The proposed imaging model is a measurement to describe the intensity changes of the pixels which are affected by rain streaks. This imaging equation has capability to explain the limitation of the existing models which can only process some special kind of raindrops, and also the imaging model is able to overcome those limitations. Thereafter, a method based on this imaging model is proposed to detect the rain-affected moving objects. Finally, the experimental results show that the imaging model has capability to describe the imaging properties of raindrops, and it has stronger robustness and better performance for outdoor vision systems than the traditional methods.