Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

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Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications

Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic

Anomaly detection in trajectory data for surveillance applications

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

Conformal anomaly detection : Detecting abnormal trajectories in surveillance applications

Human operators of modern surveillance systems are confronted with an increasing amount of trajectory data from moving objects, such as people, vehicles, vessels, and aircraft. A large majority of these trajectories reflect routine traffic and are uninteresting. Nevertheless, some objects are engaged in dangerous, illegal or otherwise interesting activities, which may manifest themselves as unusual and abnormal trajectories. These anomalous trajectories can be difficult to detect by human operators due to cognitive limitations. In this thesis, we study algorithms for the automated detection of anomalous trajectories in surveillance applications. The main results and contributions of the thesis are two-fold. Firstly, we propose and discuss a novel approach for anomaly detection, called conformal anomaly detection, which is based on conformal prediction (Vovk et al.). In particular, we propose two general algorithms for anomaly detection: the conformal anomaly detector (CAD) and the computationally more efficient inductive conformal anomaly detector (ICAD). A key property of conformal anomaly detection, in contrast to previous methods, is that it provides a well-founded approach for the tuning of the anomaly threshold that can be directly related to the expected or desired alarm rate. Secondly, we propose and analyse two parameter-light algorithms for unsupervised online learning and sequential detection of anomalous trajectories based on CAD and ICAD: the sequential Hausdorff nearest neighbours conformal anomaly detector (SHNN-CAD) and the sequential sub-trajectory local outlier inductive conformal anomaly detector (SSTLO-ICAD), which is more sensitive to local anomalous sub-trajectories. We implement the proposed algorithms and investigate their classification performance on a number of real and synthetic datasets from the video and maritime surveillance domains. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning on video trajectories. Moreover, we demonstrate that SSTLO-ICAD is able to accurately discriminate realistic anomalous vessel trajectories from normal background traffic

Conformal prediction for distribution-independent anomaly detection in streaming vessel data

This paper presents a novel application of the theory of con-formal prediction for distribution-independent on-line learn-ing and anomaly detection. We exploit the fact that confor-mal predictors give valid prediction sets at specified confi-dence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distri-bution. If the actual observation is not included in the pos-sibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the signif-icance level as an upper bound of the probability that a nor-mal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detec-tion sensitivity is superior to that of two previously proposed methods