论文标题
全球功能聚合，以期预期事故
Global Feature Aggregation for Accident Anticipation
论文作者
论文摘要
在自主和非自治车辆中提前预期事故有助于避免事故。为了识别视频序列中的异常事件，例如流量事故，重要的是，网络要考虑给定帧中对象的交互。我们提出了一个新颖的特征聚合（FA）块，该块通过计算框架中所有对象的特征的加权总和来完善每个对象的特征。我们使用FA块以及长期内存（LSTM）网络来预测视频序列中的事故。我们报告街道事故（SA）数据集的平均平均精度（MAP）和平均时间到诊所（ATTA）。我们提出的方法通过分别基于自适应损失和基于自适应参数预测的方法的最佳结果来预测事故0.32秒和0.75秒，从而实现了风险预期的最高分数。
Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggregation (FA) block that refines each object's features by computing a weighted sum of the features of all objects in a frame. We use FA block along with Long Short Term Memory (LSTM) network to anticipate accidents in the video sequences. We report mean Average Precision (mAP) and Average Time-to-Accident (ATTA) on Street Accident (SA) dataset. Our proposed method achieves the highest score for risk anticipation by predicting accidents 0.32 sec and 0.75 sec earlier compared to the best results with Adaptive Loss and dynamic parameter prediction based methods respectively.
