Crowd anomaly detection with LSTMs using optical features and domain knowledge for improved inferring

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.