Multi-dimensional Visual Fusion: Open-Pit Mine Safety Video Summarization Approach Based on Attention Mechanism and Dynamic Feature Fusion

Video summarization technology is crucial for video surveillance applications, particularly in open-pit mine security and disaster relief contexts. This study proposes a novel video summarization model architecture that enhances and integrates multi-dimensional visual features. The model utilizes multi-dimensional visual features to predict importance scores. This method plays a crucial role in responding to and conducting rescue operations in open-pit mining disasters. In contrast to previous methods relying on singular or dual features, our approach employs a multi-dimensional attention network to extract profound semantic features from multi-dimensional visual inputs, thus producing concise and representative video summaries. To comprehensively assess the efficacy of our approach, experiments were conducted on two prominent video summarization benchmark datasets, SumMe and TVSum, yielding advancements over state-of-the-art results. In addition to architectural innovations, we introduce a fusion strategy to mitigate the impact of irrelevant or redundant features on model performance. Through this method, rescue teams can swiftly identify critical events, generate summaries, and respond promptly, thereby playing a pivotal role in open-pit mine disaster management.