Shortened versions in International Conference on Computer Vision, Nice, France, pages 432-439 and Proc. Scale-Space'03, Isle of Skye, Scotland, Springer Lecture Notes in Computer Science, volume 2695, pages 372-387.
To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.
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Demonstractions: handwavedemo.avi (4.6Mb)   walk07a_classharris.avi (2.6Mb)   walk07a_silhouette.avi (3.0Mb)   walk02a_classharris.avi (1.4Mb)   walk02a_silhouette.avi (1.5Mb)
Related projects: Recognition of human actions
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