Multimedia Data Mining for Traffic Video Sequences

In this paper, a multimedia data mining framework for discovering important but previously unknown knowledge such as vehicle identification, traffic flow, and the spatio-temporal relations of the vehicles at the intersections from traffic video sequences is proposed. The proposed multimedia data mining framework analyzes the traffic video sequences by using background subtraction, image/video segmentation, object tracking, and modeling with multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over an intersection. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data-mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-a-vis traffic operations. A real-life traffic video sequence is used to illustrate the effectiveness of the proposed multimedia data mining framework.

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