Rotation-invariant object detection using Sector-ring HOG and boosted random ferns

The histogram of oriented gradients (HOG) is widely used for image description and has proven to be very effective. In some practical applications that lack an assumption of the object’s orientation, rotation-invariant detection is of vital significance. To address this problem, this paper presents a new visual feature, Sector-ring HOG (SRHOG), which is obtained by improving the gradient binning and spatial binning based on HOG. The new feature can convert planar image rotations into cyclic shifts of the final descriptor and thereby facilitate rotated object detection. After modifying boosted random ferns in SRHOG feature domain, we further propose two strategies for rotation-invariant object detection: one depends completely on the new feature’s characteristic, and the other introduces an orientation estimation step. The former is more suitable to ‘finding objects’ and the latter can provide the higher orientation estimation accuracy. Both the use of supervised learning and working in the gradient space make our approaches effective and robust. We show these properties by thorough testing on the public Freestyle Motocross dataset and our dataset for victim detection in post-disaster rescue efforts.