软+硬注意力：一个用于人类轨迹预测和异常事件检测的LSTM框架
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection
摘要：
作为人类，我们拥有一种直观的导航能力，我们通过多年的实践掌握了这一能力;然而，现有的方法来模拟这种特性，用于各种任务，包括监测行人流量和检测异常事件，这些方法都受到了使用各种手工制作功能的限制。深度学习领域的最新研究已经证明了直接从数据中学习特征的能力;递归神经网络的相关研究已经在序列到序列的问题中显示了示例性结果，例如神经机器翻译和神经图像字幕生成。受这些方法的启发，我们提出了一种新的方法来预测未来的运动的行人给予一个简短的历史，他们的邻居，过去的行为。所提出的方法的新颖性是组合的注意力模型，它利用“软注意力”以及“硬连线”的注意力，以映射的轨迹信息从本地附近的未来位置的行人的兴趣。我们说明了如何一个简单的近似的注意力权重（即硬连线）可以合并在一起，软注意力权重，以使我们的模型适用于具有挑战性的现实世界的情况下，数百个邻居。所提出的方法的导航能力进行了测试，在两个具有挑战性的公开可用的监控数据库，我们的模型优于目前的最先进的方法。此外，我们说明了如何提出的架构可以直接应用于异常事件检测的任务，而无需手工制作的功能。
英文摘要：
As humans we possess an intuitive ability for navigation which we master through years of practice; however existing approaches to model this trait for diverse tasks including monitoring pedestrian flow and detecting abnormal events have been limited by using a variety of hand-crafted features. Recent research in the area of deep-learning has demonstrated the power of learning features directly from the data; and related research in recurrent neural networks has shown exemplary results in sequence-to-sequence problems such as neural machine translation and neural image caption generation. Motivated by these approaches, we propose a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour. The novelty of the proposed method is the combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest. We illustrate how a simple approximation of attention weights (i.e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours. The navigational capability of the proposed method is tested on two challenging publicly available surveillance databases where our model outperforms the current-state-of-the-art methods. Additionally, we illustrate how the proposed architecture can be directly applied for the task of abnormal event detection without handcrafting the features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
