| Summary: | 博士 === 國立臺北科技大學 === 電腦與通訊研究所 === 102 === Automated motion detection, which segments moving objects from video streams, is the key technology of traffic surveillance systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publications about wireless video communication. Moreover, the visibility of videos will generally become degraded when captured during inclement weather conditions, such as haze and sandstorm. Video degradation can cause problems for automated motion detection, which must operate under a wide range of weather conditions. This paper presents a new motion detection approach that is based on the Fisher’s linear discriminant-based dual dark channel prior and the cerebellar-model-articulation-controller artificial neural network to completely and accurately detect moving objects in such conditions. The detection results show that our proposed approach is capable of performing with higher efficacy when compared with the results produced by other state-of-the-art approaches in variable bit-rate video streams over changing weather conditions. Both qualitative and quantitative evaluations support this claim; for instance, the proposed approach achieves Similarity and F1 accuracy rates that are 76.40% and 84.37% higher than those of existing approaches, respectively.
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