Asynchronous sensor fusion for multiple object tag-less activity tracking in manufacturing

Centralized fusion ensures minimal information loss and maximizes the effectiveness of state estimation. Statistically, it is the optimal solution for all sensor fusion configurations. In this paper, we introduce a local-sensor-driven asynchronous low-level centralized fusion methodology that seamlessly integrates radar and camera data at the level of detections from each sensor. For a local-sensor-driven asynchronous system, detections from the two sensing modalities with different sampling rates are transmitted to a centralized filter, which is updated whenever it receives a measurement. We implemented the proposed algorithm and validated the results using real data from manufacturing and industrial work sites. The data was obtained by Plato System's Argus perception system, which combines high-resolution imaging mm-wave radar with camera sensors to provide indoor and outdoor activity tracking. We further compare the fusion results with vision-only MOT, as well as track-level fusion (track-to-track fusion).