Simulation and application of COVID-19 compartment model using physic-informed neural network

In this work, SVEIDR model and its variants (Aged, Vaccination-structured models) are introduced to encode the effect of social contact for different age groups and vaccination status. Then we implement the Physic-Informed Neural Network on both simulation and real-world data. Results including the spread and forecasting analysis of COVID-19 learned from the neural network are shown in the paper.

Multiview Detection with Cardboard Human Modeling

Multiview detection uses multiple calibrated cameras with overlapping fields of views to locate occluded pedestrians. In this field, existing methods typically adopt a "human modeling - aggregation" strategy. To find robust pedestrian representations, some intuitively use locations of detected 2D bounding boxes, while others use entire frame features projected to the ground plane. However, the former does not consider human appearance and leads to many ambiguities, and the latter suffers from projection errors due to the lack of accurate height of the human torso and head. In this paper, we propose a new pedestrian representation scheme based on human point clouds modeling. Specifically, using ray tracing for holistic human depth estimation, we model pedestrians as upright, thin cardboard point clouds on the ground. Then, we aggregate the point clouds of the pedestrian cardboard across multiple views for a final decision. Compared with existing representations, the proposed method explicitly leverages human appearance and reduces projection errors significantly by relatively accurate height estimation. On two standard evaluation benchmarks, the proposed method achieves very competitive results.

Voxelized 3D Feature Aggregation for Multiview Detection

Multi-view detection incorporates multiple camera views to alleviate occlusion in crowded scenes, where the state-of-the-art approaches adopt homography transformations to project multi-view features to the ground plane. However, we find that these 2D transformations do not take into account the object's height, and with this neglection features along the vertical direction of same object are likely not projected onto the same ground plane point, leading to impure ground-plane features. To solve this problem, we propose VFA, voxelized 3D feature aggregation, for feature transformation and aggregation in multi-view detection. Specifically, we voxelize the 3D space, project the voxels onto each camera view, and associate 2D features with these projected voxels. This allows us to identify and then aggregate 2D features along the same vertical line, alleviating projection distortions to a large extent. Additionally, because different kinds of objects (human vs. cattle) have different shapes on the ground plane, we introduce the oriented Gaussian encoding to match such shapes, leading to increased accuracy and efficiency. We perform experiments on multiview 2D detection and multiview 3D detection problems. Results on four datasets (including a newly introduced MultiviewC dataset) show that our system is very competitive compared with the state-of-the-art approaches. %Our code and data will be open-sourced.Code and MultiviewC are released at https://github.com/Robert-Mar/VFA.