In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.

Least visible path analysis is a basic function in terrain visibility analysis. However, existing least visible path planning is constrained to least-cost path computing on a cost surface got from visibility information of all the terrain points on digital elevation model. This kind of method ignores the overlapped part of the adjacent points' view sheds. This paper discussed the least visual path problem and proposed a particle swarm optimization algorithm using the amalgamation of view shed to find an optimal covert path. The experimental results show that the new method produces more accurate least visible paths than the traditional one does.