MPLBoost-based mixture model for effective human detection with Deformable Part Model

Chaoran Gu, Luntian Mou, Yonghong Tian, Tiejun Huang

Abstract: The Deformable Part Model has shown high accuracy in tackling certain occlusion or deformations of objects such as cars and bikes. However, as for human category characterized by a larger number of articulated parts and more significant appearance variations, its performance gain is not so remarkable. To address this issue, we propose an MPLBoost-based mixture model which splits data into coherent groups and trains one root classifier for each, resulting in automated selection of discriminative root models and better representation of intra-class variations through visual feature clustering. Based on this boosting framework, multiple complementary features are combined to capture shape, texture and color information. Experimental results demonstrate that the proposed model can achieve an impressive performance improvement, especially in handling larger variations of human poses and viewpoints.