Recognizing Human Action from Videos Using Histograms of Visual Words

Abstract

We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel “bag-of-words” representation, where each frame corresponds to a “word”. The major difference between our model and previous “bag-of-words” models for recognition problems in computer vision is that, a “word” in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.