Multi-object Spatial-Temporal Anomaly Detection Using an LSTM-Based Framework

摘要

Spatial-temporal anomaly detection methods are mostly used for single object, but rarely for multiple objects with changing positions. This problem is often encountered in multi-player online battle arena (MOBA) games, train control systems and modern battlefield command systems, and so on. However, due to the time dependence, object correlation and Display Constraint, there are few methods for solving such problem properly. In this paper, we defined the problem of multi-object spatial-temporal anomaly detection with Display Constraint in detail. To address this problem, we proposed a long short-term memory (LSTM)-based framework. First, we proposed a Display Constraint Graph to represent location relationship and designed an LSTM framework to calculate the reconstruction error. Then we used the DCG based anomaly score to discriminate abnormal subsequences and objects. We applied this method to 18 MOBA game data streams, and achieved better results than traditional methods.