Recognizing human actions using bag-of-features and Intersection Kernel Support Vector Machines

This paper addresses the problem of human action recognition by introducing a new representation of image sequences as a collection of spatio-temporal events that are localized at interest point. The interest points are detected by the SIFT detector and a spatio-temporal interest point detector. We proposed a new bag of words approach to represent videos in two different models. Intersection Kernel Support Vector Machines is used for classification. We also present action classification results on two different datasets. Our results are either comparable to previous published results on these datasets.