Corpus ID: 12787039

Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques

  title={Anomaly Detection Based on Trajectory Analysis Using Kernel Density Estimation and Information Bottleneck Techniques},
  author={Yuejun Guo and Qing Xu and Yu Yang and Sheng Liang and Y. Liu and M. Sbert},
In this paper, we propose a new technique to enhance the trajectory shape analysis by explicitly considering the speed attribute of trajectory data, as an effective and efficient way for anomaly detection. An object motion trajectory is mathematically represented by the Kernel Density Estimation, taking into account both the shape of the trajectory and the speed of the moving object. An unsupervised clustering algorithm, using the Information Bottleneck method, is employed for the trajectory… Expand
Unsupervised Anomalous Trajectory Detection for Crowded Scenes
  • Deepan Das, Deepak Mishra
  • Computer Science, Engineering
  • 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)
  • 2018
An improved clustering based, unsu-pervised anomalous trajectory detection algorithm for crowded scenes that performs well to detect the expected anomalous trajectories from the scene. Expand
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This paper proposes a new strategy for abstracting and restoring trajectories from the perspective of signal processing, and is the first attempt to deploy the group-based signal filtering technique in the context of dealing with trajectory data. Expand
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This work addresses the challenge to detect anomalous behavior without knowing the planned flight trajectory by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. Expand
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A novel 3D visualization tool is proposed, which comprehensively illustrates the Agglomerative Information Bottleneck (AIB) based clustering scheme, to help users understand the clustering approach vividly and clearly. Expand
ePubWU Institutional Repository
Process mining aims at discovering processes by extracting knowledge from event logs. Such knowledge may refer to different business process perspectives. The organisational perspective deals, amongExpand
A Novel Anomaly Detection Method for Aircraft Trajectory in Terminal Airspace
  • Li-Jing Chen, Wei-Li Zeng, Zheng-Feng Xu, Ai-Wen Yang, Zhao Yang
  • Computer Science
  • 2020


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This article proposes and investigates the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) and the discords algorithm, a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. Expand
A system for learning statistical motion patterns
Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction. Expand
Multifeature Object Trajectory Clustering for Video Analysis
Experimental results show that the proposed approach outperforms state-of-the-art algorithms both in terms of accuracy and robustness in discovering common patterns in video as well as in recognizing outliers. Expand
Using circular statistics for trajectory shape analysis
This paper proposes to model the shape of a single trajectory as a sequence of angles described using a mixture of Von Mises (MoVM) distribution, and a complete EM (expectation maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Expand
An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval
This paper proposes an incremental version of a DPMM-based clustering algorithm and applies it to cluster trajectories, and a trajectory-based video retrieval model is developed. Expand
Trajectory-Based Anomalous Event Detection
The proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring, based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories. Expand
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
This paper presents novel classification algorithms for recognizing object activity using object motion trajectory, and uses hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology. Expand
Learning object motion patterns for anomaly detection and improved object detection
The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback and successfully used the proposed scene model to detect local as well as global anomalies in object tracks. Expand
Multi feature path modeling for video surveillance
This paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene that has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversed spatially proximal paths but having different spatio-temporal characteristics. Expand
Application of the self-organising map to trajectory classification
It is shown that analysis of trajectories may be carried out in a model-free fashion, using self-organising feature map neutral networks to learn the characteristics of normal trajectories, and to detect novel ones. Expand