Let’s consider two objectives when estimating hand postures

Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis, Alan Wee Chung Liew

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hand posture estimation is an important step in hand gesture detection. It refers to the process of modeling hand in computer to accurately represent the actual hand obtained from an acquisition device. In the literature, several objective functions (mostly based on silhouette or point cloud) have been used to formulate and solve the problem of hand posture estimation as a minimisation problem using stochastic or deterministic algorithms. The main challenge is that the objective function is computationally expensive. In the case of using point clouds, decreasing the number of points results in a better computational cost, but it decreases the accuracy of hand posture estimation. We argue in this paper that hand posture estimation is a bi-objective problem with two conflicting objectives: minimising the error versus minimising the number of points in the point cloud. As an early effort, this paper first formulates hand posture estimation as a bi-objective optimisation problem and then approximates its true Pareto optimal front with an improved Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. The proposed algorithm is used to determine the Pareto optimal front for 16 hand postures and compared with the original MOPSO. The results proved that the objectives are in conflict and the improved MOPSO outperforms the original algorithm when solving this problem.

Original languageEnglish
Title of host publicationAI 2017
Subtitle of host publicationAdvances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings
EditorsWei Peng, Damminda Alahakoon, Xiaodong Li
PublisherSpringer Verlag
Pages119-130
Number of pages12
ISBN (Print)9783319630038
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event30th Australasian Joint Conference on Artificial Intelligence, AI 2017 - Melbourne, Australia
Duration: 19 Aug 201720 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10400 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th Australasian Joint Conference on Artificial Intelligence, AI 2017
CountryAustralia
CityMelbourne
Period19/08/1720/08/17

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Keywords

  • Hand posture estimation
  • MOPSO
  • Mulit-objective particle swarm optimisation
  • Multi-objective optimisation

Cite this

Saremi, S., Mirjalili, S., Lewis, A., & Liew, A. W. C. (2017). Let’s consider two objectives when estimating hand postures. In W. Peng, D. Alahakoon, & X. Li (Eds.), AI 2017: Advances in Artificial Intelligence - 30th Australasian Joint Conference, Proceedings (pp. 119-130). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10400 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-63004-5_10