Feature-Centric Efficient Subwindow Search
Many object detection systems rely on linear classifiers embedded in a sliding-window scheme. Such exhaustive search involves massive computation. Efficient Subwindow Search (ESS)  avoids this by means of branch and bound. However, ESS makes an unfavourable memory tradeoff. Memory usage scales with both image size and overall object model size. This risks becoming prohibitive in a multi-class system. In this paper, we make the connection between sliding-window and Hough-based object detection explicit. Then, we show that the feature-centric view of the latter also nicely fits with the branch and bound paradigm, while it avoids the ESS memory tradeoff. Moreover, on-line integral image calculations are not needed. Both theoretical and quantitative comparisons with the ESS bound are provided, showing that none of this comes at the expense of performance.