標題: An AdaBoost Object Detection Design for Heterogeneous Computing with OpenCL
作者: Cheng, Bing-Yang
Lee, Jui-Sheng
Guo, Jiun-In
交大名義發表
National Chiao Tung University
公開日期: 2015
摘要: 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.
URI: http://hdl.handle.net/11536/135814
ISBN: 978-1-4799-8745-0
期刊: 2015 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW)
起始頁: 286
結束頁: 287
顯示於類別:會議論文