Inspired by the human visual system, visual attention (VA) models seem to provide solutions to problems of semantic image understanding by selecting only a small but representative fraction of visual input to process. Having proposed a spatiotemporal VA model for video processing in the past, we propose considerable enhancements in this paper, including the use of steerable filters for 3D orientation estimation, and of PCA for fusion of features for the construction of saliency volumes. We further employ segmentation and feature extraction on salient regions to provide video classification using an SVM classifier. Finally, we provide results on sports video classification and comment on the usefulness of spatiotemporal VA for such purposes.