Motion-alert: Automatic anomaly detection in massive moving objects

Xiaolei Li, Jiawei Han, Sangkyum Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. With such data, it becomes possible to automatically identify suspicious behavior in object movements. Anomaly detection in massive sets of moving objects has many important applications, especially in surveillance, law enforcement, and homeland security. Due to the sheer volume of spatiotemporal and non-spatial data (such as weather and object type) associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies in complex scenarios. The problem is further complicated by the fact that anomalies may occur at various levels of abstraction and be associated with different time and location granularities. In this paper, we analyze the problem of anomaly detection in moving objects and propose an efficient and scalable classification method, Motion-Alert, which proceeds with the following three steps. 1. Object movement features, called motifs, are extracted from the object paths. Each path consists of a sequence of motif expressions, associated with the values related to time and location. 2. To discover anomalies in object movements, motif-based generalization is performed that clusters similar object movement fragments and generalizes the movements based on the associated motifs. 3. With motif-based generalization, objects are put into a multi-level feature space and are classified by a classifier that can handle high-dimensional feature spaces. We implemented the above method as one of the core components in our moving-object anomaly detection system, motion-alert. Our experiments show that the system is more accurate than traditional classification techniques.

Original languageEnglish (US)
Title of host publicationIntelligence and Security Informatics - IEEE International Conference on Intelligence and Security Informatics, ISI 2006, Proceedings
Number of pages12
ISBN (Print)3540344780, 9783540344780
StatePublished - 2006
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2006 - San Diego, CA, United States
Duration: May 23 2006May 24 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3975 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2006
Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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