Change search
ReferencesLink to record
Permanent link

Direct link
Anomaly detection in trajectory data for surveillance applications
Örebro University, School of Science and Technology.
2011 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Abnormal behaviour may indicate important objects and events in a wide variety of domains. One such domain is intelligence and surveillance, where there is a clear trend towards more and more advanced sensor systems producing huge amounts of trajectory data from moving objects, such as people, vehicles, vessels and aircraft. In the maritime domain, for example, abnormal vessel behaviour, such as unexpected stops, deviations from standard routes, speeding, traffic direction violations etc., may indicate threats and dangers related to smuggling, sea drunkenness, collisions, grounding, hijacking, piracy etc. Timely detection of these relatively infrequent events, which is critical for enabling proactive measures, requires constant analysis of all trajectories; this is typically a great challenge to human analysts due to information overload, fatigue and inattention. In the Baltic Sea, for example, there are typically 3000–4000 commercial vessels present that are monitored by only a few human analysts. Thus, there is a need for automated detection of abnormal trajectory patterns. In this thesis, we investigate algorithms appropriate for automated detection of anomalous trajectories in surveillance applications. We identify and discuss some key theoretical properties of such algorithms, which have not been fully addressed in previous work: sequential anomaly detection in incomplete trajectories, continuous learning based on new data requiring no or limited human feedback, a minimum of parameters and a low and well-calibrated false alarm rate. A number of algorithms based on statistical methods and nearest neighbour methods are proposed that address some or all of these key properties. In particular, a novel algorithm known as the Similarity-based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) is proposed. This algorithm is based on the theory of Conformal prediction and is unique in the sense that it addresses all of the key properties above. The proposed algorithms are evaluated on real world trajectory data sets, including vessel traffic data, which have been complemented with simulated anomalous data. The experiments demonstrate the type of anomalous behaviour that can be detected at a low overall alarm rate. Quantitative results for learning and classification performance of the algorithms are compared. In particular, results from reproduced experiments on public data sets show that SNN-CAD, combined with Hausdorff distance  for measuring dissimilarity between trajectories, achieves excellent classification performance without any parameter tuning. It is concluded that SNN-CAD, due to its general and parameter-light design, is applicable in virtually any anomaly detection application. Directions for future work include investigating sensitivity to noisy data, and investigating long-term learning strategies, which address issues related to changing behaviour patterns and increasing size and complexity of training data.

Place, publisher, year, edition, pages
Örebro: Örebro universitet , 2011. , 130 p.
, Studies from the school of science and technology at Örebro university, 19
Keyword [en]
Anomaly detection, trajectory analysis, statistical methods, conformal prediction, automated surveillance
National Category
Computer Science
Research subject
Computer and Systems Science
URN: urn:nbn:se:oru:diva-17235OAI: diva2:440646
Available from: 2011-09-13 Created: 2011-09-13 Last updated: 2011-09-13Bibliographically approved

Open Access in DiVA

fulltext(9444 kB)1915 downloads
File information
File name FULLTEXT02.pdfFile size 9444 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Laxhammar, Rikard
By organisation
School of Science and Technology
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 1921 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 555 hits
ReferencesLink to record
Permanent link

Direct link