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Amodal recognition is the ability of the system to detect occluded objects.Most SOTA Visual Recognition systems lack the ability to perform amodalrecognition. Few studies have achieved amodal recognition through passiveprediction or embodied recognition approaches. However, these approaches sufferfrom challenges in real-world applications, such as dynamic obstacles. Wepropose SeekNet, an improved optimization method for amodal recognition throughembodied visual recognition. Additionally, we implement SeekNet for socialrobots, where there are multiple interactions with crowded pedestrians. We alsodemonstrate the benefits of our algorithm on occluded human detection andtracking over other baselines. Additionally, we set up a multi-robotenvironment with SeekNet to identify and track visual disease markers forairborne disease in crowded areas. We conduct our experiments in a simulatedindoor environment and show that our method enhances the overall accuracy ofthe amodal recognition task and achieves the largest improvement in detectionaccuracy over time in comparison to the baseline approaches.

SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation