TY - GEN
T1 - Let’s consider two objectives when estimating hand postures
AU - Saremi, Shahrzad
AU - Mirjalili, Seyedali
AU - Lewis, Andrew
AU - Liew, Alan Wee Chung
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Hand posture estimation
KW - MOPSO
KW - Mulit-objective particle swarm optimisation
KW - Multi-objective optimisation
UR - http://www.scopus.com/inward/record.url?scp=85026729226&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-63004-5_10
DO - 10.1007/978-3-319-63004-5_10
M3 - Conference contribution
AN - SCOPUS:85026729226
SN - 9783319630038
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 119
EP - 130
BT - AI 2017
A2 - Peng, Wei
A2 - Alahakoon, Damminda
A2 - Li, Xiaodong
PB - Springer Verlag
T2 - 30th Australasian Joint Conference on Artificial Intelligence, AI 2017
Y2 - 19 August 2017 through 20 August 2017
ER -