A novel rotation adaptive object detection method based on pair Hough model

This paper proposes a novel Hough-based object shape representation model called Pair Hough Model (PHM) and its corresponding object detection framework. PHM constructs the voting models implicitly with automatically detected interest points and their local descriptors for unseen object categories. In addition, by casting votes according to key point pairs instead of individual key points and taking the orientations of objects as well as their sizes into consideration, PHM can recognize and localize objects after their scaling and/or rotation, which makes it suitable for processing images with major rotations such as pictures taken by mobile devices. Evaluation experiments proved that PHM does not need to be trained on rotated images to recognize rotated objects, and PHM achieved comparable results to the state-of-the-art methods on several widely used public data sets.