@inproceedings{de51f3f0c09a43289c23fa882be38831,
title = "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 realtime 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.",
keywords = "Algorithm design and analysis, Central Processing Unit, Dynamic scheduling, Face detection, Graphics processing units, Heuristic algorithms, Object detection",
author = "Cheng, {Bing Yang} and Lee, {Jui Sheng} and Jiun-In Guo",
year = "2015",
month = aug,
day = "20",
doi = "10.1109/ICCE-TW.2015.7216901",
language = "English",
series = "2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "286--287",
booktitle = "2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015",
address = "United States",
note = "2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 ; Conference date: 06-06-2015 Through 08-06-2015",
}