Automated spatiotemporal scaling for video generalization

We present a technique for the summarization and spatiotemporal scaling of video content. A self organizing map (SOM) neural network can be used to acquire a rough generalization of the spatiotemporal trajectories of moving objects, in the form of few selected nodes along these trajectories. We introduce a hybrid technique, combining SOM with geometric analysis to properly densify these nodes, to better represent the spatiotemporal behavior of objects. This allows us to bypass problems inherently associated with parameter selection in SOM. We also demonstrate how spatiotemporal scaling supports the analysis of behavioral patterns. The paper shows that our novel technique is a powerful tool for the extraction of generalized information from complex trajectories, displaying high invariance to noise and information gaps in the video stream. Experimental results demonstrate the accuracy potential of our generalization technique