A Novel Deep Learning Framework for Economic Video Analysis and Tactical Insight Extraction

This paper presents a novel deep learning framework for video analysis focused on automated key object detection and tactical action recognition within economic activity contexts. The proposed system integrates enhanced motion estimation for robust tracking of functional objects and state-of-the art 3D pose estimation to extract participant postures relevant to economic decision-making behavior. A deep semantic tactical ontology is employed to model the complex relationships between individuals, objects, and their actions, enabling interpretable and rule-based tactical insight extraction for economic interaction patterns beyond conventional classification. Evaluations conducted on benchmark datasets demonstrate high accuracy with approximately 91% in object detection and 96% in action recognition, highlighting the framework’s applicability to dynamic economic environments involving multi-agent interactions. Comparative analysis against baseline methods shows the effectiveness of the framework in handling complex scenarios with occlusions and rapidly changing economic behaviors. Future work will focus on enhancing preprocessing techniques, automating ontology rule learning, and extending the approach to a wider range of economically oriented domains. This research contributes to advancing intelligent analytics by bridging deep learning with semantic reasoning, fostering improved real-time tactical feedback and decision support in economic environments.