Abstract. This paper shows a concrete example of the use of graph mining for tracking objects in videos with moving cameras and without any contextual information on the objects to track. To make the mining algorithm efficient, we benefit from a video representation based on dynamic (evolving through time) planar graphs. We then define a number of constraints to efficiently find our so-called spatio-temporal graph patterns. Those patterns are linked through an occurrences graph to allow us to tackle occlusion or graph features instability problems in the video. Experiments on synthetic and real videos show that our method is effective and allows us to find relevant patterns for our tracking application.
Dynamic graph mining is the task of searching for subgraph patterns that capture the evolution of a dynamic graph. In this paper, we are interested in mining dynamic graphs in videos. A video can be regarded as a dynamic graph, whose evolution over time is represented by a series of plane graphs, one graph for each video frame. As such, subgraph patterns in this series may correspond to objects that frequently appear in the video. Furthermore, by associating spatial information to each of the nodes in these graphs, it becomes possible to track a given object through the video in question. We present, in this paper, two plane graph mining algorithms, called Plagram and DyPlagram, for the extraction of spatiotemporal patterns. A spatiotemporal pattern is a set of occurrences of a given subgraph pattern which are not too far apart w.r.t time nor space. Experiments demonstrate that our algorithms are effective even in contexts where general-purpose algorithms would not provide the complete set of frequent subgraphs. We also show that they give promising results when applied to object tracking in videos.
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