Multimedia data mining for traffic video sequences
Shu‐Ching Chen, Mei‐Ling Shyu, Chengcui Zhang, Jeff Strickrott · 2001
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--vis traffic operations. A real-life traffic video sequence is used to illustrate the effectiveness of the proposed multimedia data mining framework. KEY WORDS: Multimedia data mining, spatiotemporal relationships, multimedia augmented transition network (MATN), object tracking. 1.