Autonomous multi-scale object detection with hough forests

The objective of this work is to detect a class of objects in images or video using multi-scale voting with random Hough Forests. Hough Forests have several nice properties, including that an implicit shape model is automatically learned from cropped images of a particular class of object and the voting induced by a Hough technique allows the detection method to handle partial occlusions. Typical Hough Forest voting is however scale sensitive when it comes to both training and testing. Currently, searching for multiple scales for an object size is achieved by re-running the detection routine for a given image at numerous manually provided input scales. This work will demonstrate that manually input scale parameters can lower detection rates if all scales in the test set are not accounted for. The novelty of our proposed work is in the creation of an autonomous scale estimation and multi-scalar detection Hough Forest voting technique. The technique proposed to accomplish the automatic scale estimation is to view votes as not votes for discrete locations, but rather as voting rays. The intersection of these rays can then be used to automatically determine the estimated object's center and scale.