Human detection for multiple pose by boosted randomized trees

In this paper we propose a robust pose invariant human detection framework. Most of the existing human detection frameworks assume a standing posture and needing a separate detectors for supporting other human postures. We propose a single framework with a hierarchical tree structure that can detect various poses. The proposed method is based on Randomized trees. Candidate features are selected as shown below, to learn high performing decision trees, 1)each node of the decision tree is constrained with classes based on class likelihood, 2)effective features are pre-selected with Joint Boosting for the above classes, 3)the candidate features are randomly generated based on these effective features. From 1) and 2), the root nodes can be trained for discriminating the human from the background, and leaf nodes can be trained for specific poses. Performance comparison was performed for various poses that arise for a “shopping scenario”, and the proposed method outperformed other multi-class classifiers based on Joint boosting, Randomized trees and Adatree.