Video retrieval by spatial and temporal structure of trajectories

Our goal is to enable queries about the motion of objects in a video sequence. Tracking objects in video is a difficult task, involving signal analysis, estimation and often semantic information particular to the targets. That is not our focus-rather, we assume that tracking is done, and turn to the task of representing the motion for query. The position over time of an object result in a motion trajectory, i.e., a sequence of locations. We propose a novel representation of trajectories: we use the path and speed curves as the motion representation. The path curve records the position of the object while the speed curve records the magnitude of its velocity. This separates positional information from temporal information, since position may be more important in specifying a trajectory than the actual velocity of a trajectory. Velocity can be recovered from our representation. We derive a local geometric description of the curves invariant under scaling and rigid motion. We adopt a warping method in matching so that it is roust to variation in feature vectors. We show that R-trees can be used to index the multidimensional features so that search will be efficient and scalable to a large database.