Scale Proportionate Histograms of Oriented Gradients for Object Detection in Co-Registered Visual and Range Data

Research in automated object detection has mainly addressed detection in 2-D intensity images. The best performing systems exhibit a high degree of robustness to variation in object scale, rotation and viewpoint. Depending on the context of the detection task, rotation and viewpoint can be controlled for, but estimating object scale is usually not possible without additional sensing apparatus. Objects must be searched for at all scales where they are likely to be present. Range sensors allow for direct distance measurements to be taken and the generation of range maps or point clouds that when co-registered against standard 2-D visual images create 3-D images. Knowledge of depth obviates the need to search over the entire scale space for an object, but this also allows for the object's scale itself to be incorporated into the detection scheme. Most existing object detection schemes use range only to constrain the detection search space or to discard detections that do not match the expected object scale. This paper presents a novel feature extraction and detection method that calculates scale proportionate histograms of oriented gradients (Pro-HOG) by exploiting known depth in an image. Pro-HOG is evaluated against a reference implementation of the original histograms of oriented gradients (HOG) feature extractor for detection of images of cars in the PASCAL Visual Object Classes 2007 dataset. High quality detection results are achieved on the real world Earthmine dataset using a Pro-HOG feature detector trained with the PASCAL VOC 2007 dataset. It is demonstrated that Pro-HOG's detection accuracy is comparable to HOG but at much reduced computational overhead.