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.
Random Forest SIFT Feature Discriminative Codebook Model Probabilistic Hough Vote
Zuhua Liu Huilin Xiong
Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and In Department of Automation, Shanghai Jiao Tong University,and Key Laboratory of System Control and Inf
国际会议
三亚
英文
1074-1078
2012-01-06（万方平台首次上网日期，不代表论文的发表时间）