Fast and Accurate Object Detection Based on Binary Co-occurrence Features

In this paper, we propose a fast and accurate object detection algorithm based on binary co-occurrence features. In our method, co-occurrences of all the possible pairs of binary elements in a block of binarized HOG are enumerated by logical operations, i.g. circular shift and XOR. This resulted in extremely fast co-occurrence extraction. Our experiments revealed that our method can process a VGA-size image at 64.6 fps, that is two times faster than the camera frame rate (30 fps), on only a single core of CPU (Intel Core i7-3820 3.60 GHz), while at the same time achieving a higher classification accuracy than original (real-valued) HOG in the case of a pedestrian detection task.