Abnormal Trajectory Detection Based on a Sparse Subgraph

Zhao, Xuehua; Rao, Yuanqi; Cai, Jianghui; Ma, Wenqiang

doi:10.1109/access.2020.2972299

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…Trajectory Similarity Measure. Most trajectory data analysis tasks require computing trajectory similarity measurements, such as trajectory clustering [19], transforming data for privacy-preservation [20], movement pattern mining [21], and abnormal trajectory detection [22]. Traditional trajectory measurement techniques such as EDR (edit distance on real sequence), LCSS (longest common subsequence), and DTW (Dynamic Time Warping) compute the overall trajectory similarity by analyzing each trajectory as a whole rather than considering subtrajectories or random trajectory points.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Wang

Niu

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

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Section: Related Workmentioning

confidence: 99%

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Wang

Niu

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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“…Chen et al [4] built an inverted index mechanism to get better retrieval performance. Zhang, Zhao et al [14], [15] used the graphbased method for detection.…”

Section: A Anomalous Trajectory Detection Based On Trajectory's Physical Characteristicsmentioning

confidence: 99%

UT-ATD: Universal Transformer for Anomalous Trajectory Detection by Embedding Trajectory Information

Zhang¹

2021

International Conferences on Distributed Multimedia Systems

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Due to the development of the transportation industry, a large amount of trajectory data is pouring into the Internet all the time. Based on these trajectory data, anomalous trajectory detection technology provides great support for traffic safety assurance and traffic risk prediction. Most existing anomalous trajectory detection methods are based on trajectory's physical characteristics or representation learning, and they achieve good performance in a few scenarios. But they still face the following problems. (1) The imperfect utilization of trajectory points. (2) The sparsity of trajectory data, which leads to generalization issues. (3) Longer model training time consumed, which can't adapt to the large amount of trajectory data generated every day. To solve the above problems, we propose a novel anomalous trajectory detection model based on Universal Transformer, called UT-ATD. UT-ATD captures the information of trajectory positions by learning trajectory embedding for classification. UT-ATD has a faster training speed, relatively few model parameters, and sufficient portability, which are ideal for the realistic scene requirements. Our model achieves state-of-the-art performance in most aspects, and its effectiveness is verified by a series of experiments on the real-world taxi trajectory dataset.

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“…And the characteristics are sparse and diverse relative to the dimensions in a spectrum. The existing searching methods are applied to find some required data, including classification methods [3], clustering algorithms [4], outlier detection algorithms [5], association rules mining [6], etc. These algorithms exhibit good performance in various fields, including image classification [3], spectral clustering [7], credit card theft [8], and so on.…”

Section: A Motivationsmentioning

confidence: 99%

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

Yang

Cai

et al. 2020

IEEE Access

Self Cite

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In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them. The DoPS, our previous work [1], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods. INDEX TERMS SVM-Lattice, double-peaked profiles, support vector machine, formal concept lattice.

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Abnormal Trajectory Detection Based on a Sparse Subgraph

Cited by 16 publications

References 32 publications

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

UT-ATD: Universal Transformer for Anomalous Trajectory Detection by Embedding Trajectory Information

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

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