| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 102 === Vehicle detectors (VDs) are usually distributed on/under a road network to detect the traffic situation. These detectors provide global information such as flow, speed, and occupancy of vehicles. Given that the collected statistical data are difficult for citizens to interpret, we visualize the data by providing users with realistic traffic videos. That is, our system collects the surveillance videos and the VD data that represent the traffic situation of the same position. It then builds the connection between these two types of data. Considering the distribution of VDs is much denser than that of surveillance cameras, for those road segments with a VD but without a surveillance camera, one can utilize our system to synthesize videos to visually depict the traffic situations over there. To achieve this aim, we estimate vehicular SIFT flows from the video and apply a regression model to build the mapping between the SIFT flows and the VD data. After that, given by a VD dataset, our system retrieves the videos, which match the VD data, and seamlessly stitches them together to synthesize the traffic video. The evaluations and the experimental results demonstrate the feasibility of our system.
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