GPU-accelerated Height Map Estimation with Local Geometry Priors in Large Scenes

Detection and tracking of pedestrians in vast crowded areas is a complex problem addressed actively by the computer vision community. Proposed algorithms should ideally tackle issues of accuracy and speed at the same time. Lengthy computation times for high-quality optimization-based algorithms relying on multiple sensors make them impractical to use on long and detailed sequences. Hence, an efficient acceleration scheme, which preserves the overall accuracy, is vital to be considered. In the current work, we iterate various steps taken to accelerate a multi-camera pedestrian detection algorithm formulated as an optimization of a height map with local scene geometry constraints. The work is performed using the NVIDIA CUDA framework which allows us to efficiently utilize GPU processors and optimize the various memory accesses. The final results show more than 1000x speedup on real data frames. With respect to preserving the output accuracy, we achieve an accelerated output which is more than 99.9% in agreement with the original results.