An Intelligent Decision Support System for Photography Optimization in Dynamic Scenes

Photographing dynamic scenes requires rapid decisions on exposure, white balance, and composition, often challenging for users. This paper presents an intelligent decision support system (IDSS) for real-time photography optimization. The system employs deep learning to achieve context awareness through scene recognition and object tracking and then generates adaptive recommendations for camera parameters and framing strategies. By integrating perception with a model-based decision engine, the IDSS reduces user cognitive load and improves imaging consistency. Experimental results across diverse dynamic environments demonstrate superior performance compared to traditional methods. This work illustrates how artificial intelligence (AI)-driven decision support can enhance user experience in creative tasks and provides a scalable framework for intelligent photography assistance.