  This paper introduces a high efficient local spatiotemporal descriptor,
called gradient boundary histograms (GBH). The proposed GBH descriptor is built
on simple spatio-temporal gradients, which are fast to compute. We demonstrate
that it can better represent local structure and motion than other
gradient-based descriptors, and significantly outperforms them on large
realistic datasets. A comprehensive evaluation shows that the recognition
accuracy is preserved while the spatial resolution is greatly reduced, which
yields both high efficiency and low memory usage.
