With the increasing population, the probability of occurrence of different kinds of crowd anomalies gets frequent. Blockage on roads, the lighting condition, and the uneven movement of humans and vehicles makes it a tough and challenging problem. The paper proposes the combined use of a convolutional neural network and bidirectional LSTM to solve the task. CNN helps extract frame-level features of the optical flow over the video evaluated by the Lucas Kanade algorithm. A novel approach of improving the predicted class with the domain knowledge of datasets is also performed. The proposed methodology is tested on the crowd anomaly dataset's benchmark datasets, namely UCSD Ped-1 and UCSD Ped-2, and it outperforms various other existing state-of-the-art methods.

随着人口的增加，发生各种人群异常的可能性越来越高。道路上的障碍物，照明条件以及人和车辆的不均匀运动使其成为一个棘手且具有挑战性的问题。本文提出了结合使用卷积神经网络和双向LSTM来解决该任务。CNN有助于提取由Lucas Kanade算法评估的视频上光流的帧级特征。还执行了一种利用数据集的领域知识来改进预测类的新颖方法。所提出的方法已在人群异常数据集的基准数据集UCSD Ped-1和UCSD Ped-2上进行了测试，其性能优于其他各种现有的最新方法。