Dynamic Load Balancing of Parallel SURF with Vertical Partitioning

Deokho Kim, Minwoo Kim, Kyungah Kim, Minyong Sung, Won Woo Ro

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The demand for real-Time processing of robust feature detection is one of the major issues in the computer vision field. In order to comply with the requirements, in this paper a parallelization and optimization method to effectively accelerate SURF is proposed. The proposed parallelization method is developed based on a workload analysis of SURF in terms of various aspects, focusing in particular on the load balancing problem. First, the average parallel workload is divided into identical portions using the vertical partitioning method. Then, the load imbalance problem is further resolved using the dynamic partition balancing method. In addition, an optimization method is proposed together with the parallelization method to find and exclude redundant operations in SURF, thus effectively accelerating the feature detection operation when the proposed parallelization method is applied. The proposed method shows a maximum speedup of 19.21 compared to the single threaded performance on a 24-core system, achieving a maximum of 83.80 fps in a real-machine experiment, enabling real-Time processing.

Original languageEnglish
Article number6963467
Pages (from-to)3358-3370
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number12
Publication statusPublished - 2015 Dec 1

Bibliographical note

Publisher Copyright:
© 1990-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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