A COMBINATION OF DEEP LEARNING AND DENSIIY METHOD IN ANOMALOUS HUMAN TRAJECTORY DETECTION

Abnormal human trajectories in working places are often associated with problems such as terrorism, violent attacks and fire. Therefore, detecting anomalous human trajectories can improve safety and security in working areas. In this work, a novel framework of abnormal trajectory detection is proposed based on combining deep learning and density method. In particular, a Long Short-Term Memory-Autoencoder (LSTM-AE) is first applied to learn informative representations of normal trajectories. Then, the density of trajectory representation in the latent space and reconstruction error of trajectory are used to detect anomalies. A novel metric is also proposed to determine the anomaly scores of trajectories. The proposed framework is evaluated using two real trajectory datasets: the MIT Badge and the sCREEN datasets. The experimental results show that our work effectively detects anomalies, achieving a f1-score of 81.08% on the MIT Badge dataset and 89.57% on the sCREEN dataset.