NDLI: Accelerating Viola-Jones Facce Detection Algorithm on GPUs

Content Provider	IEEE Xplore Digital Library
Author	Haipeng Jia Yunquan Zhang Weiyan Wang Jianliang Xu
Copyright Year	2012
Abstract	The Viola-Jones face detection algorithm represents a class of parallel algorithms that both memory accesses and work distributions are irregular, thereby hard to obtain high performance on GPUs. Furthermore, conventional GPU programming wisdom usually guides us on how to optimize data parallel workloads with regular inputs and outputs. While how to efficiently write task-level parallelism programs with irregular workloads have not much material to reference. In this paper, we present an OpenCL-implementation of Viola-Jones face detection algorithm with high performance on both NVIDIA and AMD GPUs through five main techniques: warp size work granularity, persistent threads, Uberkernel, local and global queues. We also demonstrate the high performance of our implementation by comparing it with a well-optimized CPU version from OpenCV library. Experiment results show that the speedup reaches up to 5.193 ~35.08 times (16.91 on average) and 5.85 ~32.641 times (17.535 on average) on AMD and NVIDIA GPU respectively.
Starting Page	396
Ending Page	403
File Size	1070657
Page Count	8
File Format	PDF
ISBN	9781467321648
DOI	10.1109/HPCC.2012.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-25
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Viola-Jones Global Queues Instruction sets Parallel processing Imbalanced Computation Face Face detection Kernel Persistent Threads Local Queues Graphics processing unit
Content Type	Text
Resource Type	Article

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