Hybrid parallel cascade classifier training for object detection

Abstract

A drawback of the Viola and Jones framework for object detection in digital images is the large amount of time needed to train the underlying cascade classifiers. In this paper, we propose a novel hybrid approach for parallelizing that framework. The approach employs message passing among computers and multi-threading in the processor cores, hence its hybrid nature. In contrast to related works, which dealt with only parts the original framework, in this paper we considered the complete framework. Besides, the set of weak classifiers obtained by our parallel approach is identical to the one of a serial version. An experimental evaluation on face detection focused on speedup and scalability measures and has shown the improvements of the proposed approach over a serial implementation of the original framework.