Multi-class multi-instance boosting for part-based human detection

Yu-Ting Chen, Chu-Song Chen, Yi-Ping Hung, Kuang-Yu Chang
2009 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops  
With the purpose of designing a general learning framework for detecting human parts, we formulate this task as a classification problem over non-aligned training examples of multiple classes. We propose a new multi-class multiinstance boosting method, named MCMIBoost, for effective human parts detection in static images. MCMIBoost has two benefits. First, training examples are represented as a set of non-aligned instances, so that the alignment problem caused by human appearance variation can
more » ... e handled. Second, instead of learning part detectors individually, MCMIBoost learns a unified detector for efficient detection, and uses the feature-sharing concept to design an efficient multi-class classifier. Experiment results on MIT and INRIA datasets demonstrate the superior performance of the proposed method.
doi:10.1109/iccvw.2009.5457475 dblp:conf/iccvw/ChenCHC09 fatcat:skuhg5vpjjgv5ehjngyqpv3cvq