Bo(V)W models for object recognition from video

In this paper we introduce two novel methods for object recognition from video. Our major contributions are (i) the use of dense, overlapping local descriptors as means of accurately capturing the appearance of generic, even untextured objects, (ii) a framework for employing such sets for recognition using video, (iii) a detailed empirical examination of different aspects of the proposed model and (iv) a comparative performance evaluation on a large object database. We describe and compare two bag-of-visual-words (BoVW)-based representations of an object's appearance in a video sequence, one using a per-sequence bag-of-words and one using a set of per-frame bag-of-words. Empirical results demonstrate the effectiveness of both representations with a somewhat favourable performance of the former.