Scalable Complex Scene Understanding for Edge Computing

Nanxi Chen, Lili Liu, Xu Wang, Jiamao Li, Xiaolin Zhang

Abstract: This paper aims to develop flexible and scalable scene understanding capabilities usable on resource-constrained devices. We propose an edge computing framework to enable large-scale object detection within complex visual scenes. The framework attempts to emulate human intuition - leveraging commonsense reasoning and fast concept learning from small data. Structured knowledge about objects and their relationships guides efficient deduction of scene contents. Specifically, we develop an ontology-based scene deduction model to refine an initial scene parsing, which incorporates declarative constraints on objects, their attributes, and interactions. It represents such domain knowledge explicitly and reasons over ontological relationships. By representing this domain knowledge explicitly and reasoning over ontological relationships, the model refines an initial scene parsing to better reflect commonsense consistency. The evaluation compared the proposed method to other large-scale object detection methods. The results show that our ontology-driven deduction model scale-ups the data-driven scene parser’s capability while reducing the data and compute requirements.

External IDs:dblp:conf/icws/ChenLWLZ24