Follow
Rikard Laxhammar
Rikard Laxhammar
Acne Studios
Verified email at acnestudios.com
Title
Cited by
Cited by
Year
Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator
R Laxhammar, G Falkman, E Sviestins
2009 12th international conference on information fusion, 756-763, 2009
2462009
Anomaly detection for sea surveillance
R Laxhammar
2008 11th international conference on information fusion, 1-8, 2008
2152008
Online learning and sequential anomaly detection in trajectories
R Laxhammar, G Falkman
IEEE transactions on pattern analysis and machine intelligence 36 (6), 1158-1173, 2013
2062013
Sequential conformal anomaly detection in trajectories based on hausdorff distance
R Laxhammar, G Falkman
14th international conference on information fusion, 1-8, 2011
892011
Conformal prediction for distribution-independent anomaly detection in streaming vessel data
R Laxhammar, G Falkman
Proceedings of the first international workshop on novel data stream pattern …, 2010
642010
Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories
R Laxhammar, G Falkman
Annals of Mathematics and Artificial Intelligence 74, 67-94, 2015
622015
Conformal anomaly detection: Detecting abnormal trajectories in surveillance applications
R Laxhammar
University of Skövde, 2014
362014
Anomaly detection in trajectory data for surveillance applications
R Laxhammar
Örebro universitet, 2011
342011
An ensemble approach for increased anomaly detection performance in video surveillance data
C Brax, L Niklasson, R Laxhammar
2009 12th International Conference on Information Fusion, 694-701, 2009
182009
A joint statistical and symbolic anomaly detection system: Increasing performance in maritime surveillance
A Holst, B Bjurling, J Ekman, Å Rudström, K Wallenius, M Björkman, ...
2012 15th International Conference on Information Fusion, 1919-1926, 2012
172012
Anomaly detection
R Laxhammar
Morgan Kaufmann Publishers, 2014
152014
Online detection of anomalous sub-trajectories: A sliding window approach based on conformal anomaly detection and local outlier factor
R Laxhammar, G Falkman
IFIP International Conference on Artificial Intelligence Applications and …, 2012
152012
Artificial intelligence for situation assessment
R Laxhammar
Numerisk analys och datalogi, Kungliga Tekniska högskolan, 2007
132007
Conformal anomaly detection
R Laxhammar
Skövde, Sweden: University of Skövde 2, 2014
122014
Approaches for detecting behavioural anomalies in public areas using video surveillance data
C Brax, R Laxhammar, L Niklasson
Electro-Optical and Infrared Systems: Technology and Applications V 7113 …, 2008
32008
D4. 3. Vehicle models for fuel consumption
R Laxhammar, A Gascón-Vallbona
The system can't perform the operation now. Try again later.
Articles 1–16