An AdaBoost object detection design for heterogeneous computing with OpenCL

Cheng, Bingyang; Lee, Jui-Sheng; Guo, Jiun-In

doi:10.1109/icce-tw.2015.7216901

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2018

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“…Martínez-Zarzuela et al [6] proposed an approach for AdaBoost face detection using Haar-like features on GPU and obtained overall 3.3x speed-up. Cheng et al [7] used the techniques of scale paralleling, stage partitioning and dynamic stage scheduling on AdaBoost algorithm, solved load-unbalanced problems when realized in multicore CPU and GPU platform. Lee et al [8] accelerated the Viola-Jones face detection algorithm and achieved 6.29x speed-up with the INHA FACE dataset.…”

Section: Introductionmentioning

confidence: 99%

An OpenCL Parallelized Traffic Sign Recognition

Kang¹,

Cai²,

Guo³

et al. 2018

dtcse

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Traffic sign detection and recognition (TSDR) plays crucial roles in advanced driving assistant system(ADAS). In this work, we propose an OpenCL parallelized TSDR method to address the time-consuming challenge. The method employs AdaBoost algorithm for traffic sign detection and Fisherface algorithm for the traffic sign recognition. The Haar feature extraction and Adaboost algorithm are accelerated by sliding windows paralleling and stage classifier group scheduling strategies. The results of experimental work reveal that our approach offers about 12x speed-up for 1920x1080 resolution, which effectively compress the computation time.

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Section: Introductionmentioning

confidence: 99%