An AdaBoost Object Detection Design for Heterogeneous Computing with OpenCL

Abstract

AdaBoost classification with Haar-like features [1] is commonly adopted for object detection. Feature calculation in AdaBoost algorithm is the most time-consuming part, which occupies over 98% of the computation and cannot reach real-time processing with CPU computing only. In this paper we propose an object detection design for heterogeneous computing with OpenCL. By adopting the techniques of scale parallelizing, stage partitioning, and dynamic stage scheduling on AdaBoost algorithm, the proposed design solves load-unbalanced problems when realize in multicore CPU and GPU platform. The proposed object detection design achieves 32.5 fps at D1 resolution on an AMD A10-7850K processor.