OpenMP and GPGPU Implementations of Probabilistic Occupancy Map for Multiple Human Position Estimation

Probabilistic Occupancy Map (POM) is a method to estimate a location of multiple people, given images taken by multiple cameras from different angles set at a head level. It is useful even in the case of significant occlusion and can derive a location of people without an appearance model and prerequisite knowledge about the number of people in a detection space. However, the computation time of POM increases according to the area of detection space and number of cameras, which limits its performance. In this paper, we report performance enhancement of POM by applying OpenMP and GPGPU. As a result of evaluating test videos with different area size and cameras, we confirm that OpenMP implementation marks 1.8 to 2.7 times speedup compared to single CPU core implementation for all videos, whereas GPU implementation provides speedup in case of large grid size by a maximum of 7.6 times.