ORIGINAL ARTICLE Designing a camera placement assistance system for human motion capture based on a guided genetic algorithm Azeddine Aissaoui 1 • Abdelkrim Ouafi 1 • Philippe Pudlo 2,3,4 • Christophe Gillet 2,3,4 • Zine-Eddine Baarir 1 • Abdelmalik Taleb-Ahmed 2,3,4 Received: 1 August 2015 / Accepted: 24 March 2017 Ó Springer-Verlag London 2017 Abstract In multi-camera motion capture systems, deter- mining the optimal camera configuration (camera positions and orientations) is still an unresolved problem. At present, configurations are primarily guided by a human operator’s intuition, which requires expertise and experience, espe- cially with complex, cluttered scenes. In this paper, we propose a solution to automate camera placement for motion capture applications in order to assist a human operator. Our solution is based on the use of a guided genetic algorithm to optimize camera network placement with an appropriate number of cameras. In order to improve the performance of the genetic algorithm (GA), two techniques are described. The first is a distribution and estimation technique, which reduces the search space and generates camera positions for the initial GA population. The second technique is an error metric, which is inte- grated at GA evaluation level as an optimization function to evaluate the quality of the camera placement in a camera network. Simulation experiments show that our approach is more efficient than other approaches in terms of compu- tation time and quality of the final camera network. Keywords Multi-camera-based motion capture systems  Optimal camera configurations  Genetic algorithm  Optimization 1 Introduction Human motion capture using markers (active or passive) requires cameras to be positioned around the volume of interest so that at least two cameras can view each marker. Three-dimensional marker positions are then calculated by triangulation, and subject movements can thus be defined. When the movement is complex or is produced in the pres- ence of obstacles, it is more difficult to capture markers. Consequently, a human operator tries to find camera con- figurations that minimize marker occlusion. This task remains difficult and requires time as well as many validation tests, even in the case of an experienced expert. Camera configuration has a critical impact on the overall motion capture performance. The configuration quality to determine the marker in 3D is strongly affected by two sources of error: marker visibility and triangulation accu- racy. In order to address these error sources, the following points should be taking into consideration: 1. A Marker should not be occluded by any obstacle. 2. A Marker should be within the camera’s field of view. 3. A Marker must be visible given a camera’s resolution, the marker’s size, and its distance from the camera. 4. At least two cameras are required to reconstruct a marker. 5. Cameras should be arranged sufficiently non-parallel so that triangulation calculations are well conditioned. 6. Incorporating additional cameras leads to over condi- tioned triangulation. However, it involves a higher cost. To assist human operators in configuring camera networks and improving human motion capture, we developed a computer tool using a guided genetic algorithm to simulate the best camera network configuration. Our approach looks & Azeddine Aissaoui aissaoui_azeddine@hotmail.fr 1 LESIA Laboratory, Biskra University, BP 145 RP, 07000 Biskra, Algeria 2 UVHC, LAMIH, 59313 Valenciennes, France 3 CNRS, UMR 8201, 59313 Valenciennes, France 4 University Lille Nord de France, 59000 Lille, France 123 Virtual Reality DOI 10.1007/s10055-017-0310-7