Oblique random forest based on partial least squares applied to pedestrian detection
The increasing popularity of approaches based on random forest in computer vision tasks is due to its simplicity and flexibility with complex data. Random forest is a set of decision trees that can be divided in two subsets according to the view of the feature descriptors provided as input: orthogonal and oblique. In the former, the feature space is separated orthogonally (axis-aligned) by a single feature at a time. In the latter, it separates the space by oriented hyperplanes, which usually provides better data modeling. This work proposes a novel oblique random forest associated with Partial Least Squares to perform the oblique split. We validate the proposed approach, referred to as oRF-PLS, on the challenge INRIA Person dataset. Experimental results demonstrate that the proposed method outperforms traditional state-of-the-art detectors. In addition, we demonstrate that PLS is a more suitable choice to build oblique random forest than SVM, being faster and producing more accurate forests.