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Object Detection and Localization Using Random Forest

In this paper, we present a method for object detection and localization using the technique of random forest. In the process of supervised learning to construct random forest, we use the descriptor vectors of the SIFT features as input samples and their class information as supervised information. For each leaf node of the decision tree, the offsets of local features reach this node along with their class information, and the class information of this node are stored. Therefore, all leaf nodes construct a discriminative tree-structured codebook model. In object detection, the discriminative codebook is used to estimate the object's location via a probabilistic computation called probabilistic Hough vote. The experimental results show that our algorithm can provide a better detection results even in the complicated environment such as multi-scale, multi-perspective, occlusion and strong background noise.