论文标题
基于视觉的秋季事件检测在复杂背景下使用注意力指导的双向LSTM
Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM
论文作者
论文摘要
秋季事件检测是老年人最大的风险之一，近年来一直是孤独现场的热门研究问题。然而，关于秋季事件在复杂背景下检测的研究很少。与大多数取决于背景建模的传统背景减法方法不同，基于深度学习技术的蒙版R-CNN方法可以在噪声背景中清楚地提取移动对象。我们进一步提出了针对最终秋季事件检测的注意力指导的双向LSTM模型。为了证明效率，在公共数据集和自我构建数据集中验证了所提出的方法。与其他最先进的方法相比，对算法性能的评估表明，所提出的设计是准确且健壮的，这意味着它适合在复杂情况下秋季事件检测的任务。
Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We further propose an attention guided Bi-directional LSTM model for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.
