Rikard Laxhammar

Bio: Rikard Laxhammar is an academic researcher from University of Skövde. The author has contributed to research in topics: Anomaly detection & Conformal anomaly. The author has an hindex of 10, co-authored 13 publications receiving 610 citations. Previous affiliations of Rikard Laxhammar include Saab Automobile AB & Saab AB.

Anomaly detection in sea traffic - A comparison of the Gaussian Mixture Model and the Kernel Density Estimator

Abstract: This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model (GMM) and the adaptive Kernel Density Estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and simulated anomalous trajectories. The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Part of the explanation is that the methods are based on a rather artificial division of data into geographical cells. The paper therefore discusses other clustering approaches based on more informed features of data and more background knowledge regarding the structure and natural classes of the data.

Online Learning and Sequential Anomaly Detection in Trajectories

Abstract: Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may therefore suffer from overfitting and poorly-calibrated alarm rates. In this article, we propose and investigate the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) for online learning and sequential anomaly detection in trajectories. This is a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. The discords algorithm, originally proposed by Keogh et al. , is another parameter-light anomaly detection algorithm that has previously been shown to have good classification performance on a wide range of time-series datasets, including trajectory data. We implement and investigate the performance of SHNN-CAD and the discords algorithm on four different labeled trajectory datasets. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning during unsupervised online learning and sequential anomaly detection in trajectories.

Anomaly detection for sea surveillance

Abstract: In this paper, unsupervised clustering of normal vessel traffic patterns is proposed and implemented, where patterns are represented as the momentary location, speed and course of tracked vessels. The learnt cluster models are used for anomaly detection in sea traffic. The Gaussian Mixture Model is used as cluster model and a greedy version of the Expectation-Maximization algorithm is used as clustering algorithm. The models have been trained and evaluated using real recorded sea traffic. A qualitative analysis reveals that the most distinguishing anomalies found in the traffic are vessels crossing sea lanes and vessels traveling close to and in the opposite direction of sea lanes. In order to detect complex anomalies involving multiple vessels and/or behavior that develop over time, a more sophisticated pattern model should be developed. Yet, the generality of the proposed model is stressed, as it is potentially applicable to other domains involving surveillance of moving objects.

Sequential Conformal Anomaly Detection in trajectories based on Hausdorff distance

Abstract: Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with well-calibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameter-free dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.

Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories

Abstract: Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms often require tuning of many parameters, including ad-hoc anomaly thresholds, which may result in overfitting and high alarm rates. The main contributions of this paper are two-fold: The first is the proposal and analysis of the Inductive Conformal Anomaly Detector (ICAD), which is a general and parameter-light anomaly detection algorithm that has well-calibrated alarm rate. ICAD is a generalisation of the previously proposed Conformal Anomaly Detector (CAD) based on the concept of Inductive Conformal Predictors. The main advantage of ICAD compared to CAD is the improved computational efficiency. The only design parameter of ICAD is the Non-Conformity Measure (NCM). The second contribution of this paper concerns the proposal and investigation of the Sub-Sequence Local Outlier (SSLO) NCM, which is designed for sequential detection of anomalous sub-trajectories in the framework of ICAD. SSLO-NCM is based on Local Outlier Factor (LOF) and is therefore sensitive to local sub-trajectory anomalies. The results from the empirical investigations on an unlabelled set of vessel trajectories illustrate the most anomalous trajectories detected for different parameter values of SSLO-NCM, and confirm that the empirical alarm rate is indeed well-calibrated.

Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

Abstract: Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers).

Video-Based Abnormal Human Behavior Recognition—A Review

Abstract: Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Abstract: To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

Anomaly Detection in Streams with Extreme Value Theory

Abstract: Anomaly detection in time series has attracted considerable attention due to its importance in many real-world applications including intrusion detection, energy management and finance. Most approaches for detecting outliers rely on either manually set thresholds or assumptions on the distribution of data according to Chandola, Banerjee and Kumar. Here, we propose a new approach to detect outliers in streaming univariate time series based on Extreme Value Theory that does not require to hand-set thresholds and makes no assumption on the distribution: the main parameter is only the risk, controlling the number of false positives. Our approach can be used for outlier detection, but more generally for automatically setting thresholds, making it useful in wide number of situations. We also experiment our algorithms on various real-world datasets which confirm its soundness and efficiency.