Parallel Pipeline Volume Intersection for Real-Time 3D Shape Reconstruction on a PC Cluster

The human activity monitoring is one of the major tasks in the field of computer vision. Recently, not only the 2D images but also 3D shapes of a moving person are desired in kinds of cases, such as motion analysis, security monitoring, 3D video creation and so on. In this paper, we propose a parallel pipeline system on a PC cluster for reconstructing the 3D shape of a moving person in real-time. For the 3D shape reconstruction, we have extended the volume intersection method to the 3-base-plane volume intersection. By thus extension, the computation is accelerated greatly for arbitrary camera layouts. We also parallelized the 3-base-plane method and implemented it on a PC cluster. On each node, the pipeline processing is adopted to improve the throughput. To decrease the CPU idle time caused by I/O processing, image capturing, communications over nodes and so on, we implement the pipeline using multiple threads. So that, all stages can be executed concurrently. However, there exists resource conflicts between stages in a real system. To avoid the conflicts while keeping high percentage of CPU running time, we propose a tree structured thread control model. As a result, We achieve the performance as obtaining the full 3D volumes of a moving person at about 12 frames per second, where the voxel size is 5×5×5 [mm^3]. The effectiveness of the thread tree model in such real-time computation is also proved by the experimental results.