Journal ArticleDOI
Outlier trajectory detection through a context-aware distance
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TLDR
The main focus of the paper is to highlight a context-aware distance between trajectories, which implies a weighted average of the differences in the angle, the Euclidean distance, and the number of points in each trajectory.Abstract:
This paper presents an original method to detect anomalous human trajectories based on a new and promising context-aware distance. The input of the proposed method is a set of human trajectories from a video surveillance system. A proper representation of each trajectory is developed based on the polar coordinates of the corresponding sub-trajectories. The main focus of the paper is to highlight a context-aware distance between trajectories. This distance implies a weighted average of the differences in the angle, the Euclidean distance, and the number of points in each trajectory. The distance matrix feeds an unsupervised learning method to extract homogeneous groups (clusters) of trajectories. Finally, an outlier detection method is executed over the trajectories in each cluster. The methodology has been empirically evaluated across four experiments with both artificial and real data sets. The tests results have proved promising and illustrate the effectiveness of this approach for anomalous trajectories detection.read more
Citations
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Journal ArticleDOI
Anomalous Trajectory Detection and Classification Based on Difference and Intersection Set Distance
TL;DR: This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them, using the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories.
Journal ArticleDOI
Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges
TL;DR: Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades and is studied in detail in this work.
Journal ArticleDOI
A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection
TL;DR: This work innovatively proposes Seq2Seq model based on LSTM prediction network for trajectory modelling (SL-Modelling), and abnormal trajectory detection method with spatio-temporal and semantic information with stronger detection ability with higher accuracy and takes less time.
Journal ArticleDOI
Deep Learning Versus Traditional Solutions for Group Trajectory Outliers
TL;DR: Wang et al. as discussed by the authors introduced a new model to identify a group of trajectory outliers from a large trajectory database and proposed several algorithms. But, their work was limited to a single trajectory dataset.
Journal ArticleDOI
A Framework of Abnormal Behavior Detection and Classification Based on Big Trajectory Data for Mobile Networks
TL;DR: Wang et al. as mentioned in this paper proposed a framework for urban trajectory modeling and anomaly detection, which takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
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Distance-based outliers: algorithms and applications
TL;DR: Outlier detection can be done efficiently for large datasets, and for k-dimensional datasets with large values of k, and it is shown that outlier detection is a meaningful and important knowledge discovery task.