Memory-Enhanced Human Action Parsing and Object Interaction Segmentation in Complex Video Streams
Keywords:
video understanding, memory-augmented networks, human-object interaction, action segmentation, system architecture, fairness, edge computingAbstract
The proliferation of high-resolution surveillance, egocentric, and broadcast video data has exposed fundamental limitations in conventional architectures for human action parsing and object interaction segmentation. Short temporal windows, isolated treatment of action and object cues, and static model designs fail to capture the long-range dependencies, occlusions, and compositional dynamics that characterize complex real-world streams. This paper presents a comprehensive systems-oriented analysis of memory-enhanced frameworks that jointly model human actions and object interactions across extended temporal horizons. We argue that the integration of explicit memory modules, which can store, update, and selectively retrieve spatiotemporal context, constitutes a structural transformation rather than a mere accuracy improvement. The discussion dissects architectural trade-offs among fixed-size latent memory pools, self-attentive memory banks, and dynamic external memory controllers, linking design choices to compute cost, latency, and the capacity to sustain coherent object-action associations over thousands of frames. By embedding the technical analysis within a larger socio-technical perspective, the paper examines infrastructure requirements, edge-cloud partitioning strategies, energy sustainability, data governance, and the fairness implications of action recognition systems deployed in diverse human environments. Policy recommendations for lifecycle monitoring, bias auditing, and privacy-preserving inference are distilled from case illustrations in public safety and assistive technology domains. The paper concludes by identifying open systems challenges, including memory staleness, cross-modal alignment under distribution shift, and the need for standardized benchmarks that evaluate consistency rather than frame-level accuracy alone. The synthesis positions memory-enhanced video parsing as a critical enabler of trustworthy, scalable, and context-aware perception systems.
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