  Amodal recognition is the ability of the system to detect occluded objects.
Most SOTA Visual Recognition systems lack the ability to perform amodal
recognition. Few studies have achieved amodal recognition through passive
prediction or embodied recognition approaches. However, these approaches suffer
from challenges in real-world applications, such as dynamic obstacles. We
propose SeekNet, an improved optimization method for amodal recognition through
embodied visual recognition. Additionally, we implement SeekNet for social
robots, where there are multiple interactions with crowded pedestrians. We also
demonstrate the benefits of our algorithm on occluded human detection and
tracking over other baselines. Additionally, we set up a multi-robot
environment with SeekNet to identify and track visual disease markers for
airborne disease in crowded areas. We conduct our experiments in a simulated
indoor environment and show that our method enhances the overall accuracy of
the amodal recognition task and achieves the largest improvement in detection
accuracy over time in comparison to the baseline approaches.
