Extracting Spatiotemporal Interest Points using Global Information

Local spatiotemporal features or interest points provide compact but descriptive representations for efficient video analysis and motion recognition. Current local feature extraction approaches involve either local filtering or entropy computation which ignore global information (e.g. large blobs of moving pixels) in video inputs. This paper presents a novel extraction method which utilises global information from each video input so that moving parts such as a moving hand can be identified and are used to select relevant interest points for a condensed representation. The proposed method involves obtaining a small set of subspace images, which can synthesise frames in the video input from their corresponding coefficient vectors, and then detecting interest points from the subspaces and the coefficient vectors. Experimental results indicate that the proposed method can yield a sparser set of interest points for motion recognition than existing methods.