Local Descriptors for Spatio-Temporal Recognition
Ivan Laptev and Tony LindebergECCV'04 Workshop on Spatial Coherence for Visual Motion Analysis, (Prague, Czech Republic), May 2004.
Springer Lecture Notes in Computer Science, volume 3667, pp. 91-103.
AbstractThis paper presents and investigates a set of local space-time descriptors for representing and recognizing motion patterns in video. Following the idea of local features in the spatial domain, we use the notion of space-time interest points and represent video data in terms of local space-time events. To describe such events, we define several types of image descriptors over local spatio-temporal neighborhoods and evaluate these descriptors in the context of recognizing human activities. In particular, we compare motion representations in terms of spatio-temporal jets, position dependent histograms, position independent histograms, and principal component analysis computed for either spatio-temporal gradients or optic flow. An experimental evaluation on a video database with human actions shows that high classification performance can be achieved, and that there is a clear advantage of using local position dependent histograms, consistent with previously reported findings regarding spatial recognition.
Related projects: Recognition of human actions
Related publications: (Velocity adaptation of space-time interest points) (Recognizing Human Actions: a Local SVM Approach) (Space-time interest points) (Interest point detection and scale selection in space-time) (Velocity-adaptation of spatio-temporal receptive fields for direct recognition of activities: An experimental study) (Linear spatio-temporal scale-space) (Monograph on scale-space theory)
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