Dynamic scene modelling and anomaly detection based on trajectory analysis

Wan, Yiwen; Yang, Tze-I; Keathly, David; Buckles, Bill P.

doi:10.1049/iet-its.2012.0119

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…The minimum mean is 958, and its corresponding sample size is 4968. According to (7) and ( 8), we can obtain k ⋅ C 2 N = 4968. The approximate value of N is calculated to be 41, and MinPts = N ∕4 ≈ 10.…”

Section: Clustering Results Of Datasetmentioning

confidence: 99%

“…distances between two trajectories in a class, and the number of trajectories: N can be computed by (7):…”

Section: Outputmentioning

confidence: 99%

“…Vehicle motion trajectories containing rich temporal and spatial semantic information are the most prominent feature representations for analysing vehicle movement patterns [7]. Hence, vehicle trajectory data are commonly exploited to analyse movement patterns.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

Long

et al. 2022

IET Intelligent Trans Sys

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The density-based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real-world scenes, and the experimental results are promising and effective.

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Section: Clustering Results Of Datasetmentioning

confidence: 99%

“…distances between two trajectories in a class, and the number of trajectories: N can be computed by (7):…”

Section: Outputmentioning

confidence: 99%

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Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

Long

et al. 2022

IET Intelligent Trans Sys

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“…To calculate this homography matrix, four point correspondences of the region of interest (ROI) from the real world are required. These points could be chosen based on the practical needs of the ROI selection, and they can be extracted automatically using traffic scene recognition methods such as [34,35]. In this paper, in order to detect potential risks in non-motor lanes of the intersection, these points are extracted manually from zebra crossing signs near the targeted bicycle lane.…”

Section: Preprocessingmentioning

confidence: 99%

Video‐based method for detecting potential risks among multiple electric bikes

Xia

Gao

Tian

et al. 2019

IET Intelligent Transport Systems

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“…Morris & Trivedi (2011) used the weighted average of trajectories of each cluster to form the path model for that cluster. Based on the dominant set clustering approach, Yiwen et al (2014) proposed a system that obtains the scene structure from clustered trajectories.…”

Section: Introductionmentioning

confidence: 99%

Novel trajectory clustering method based on distance dependent Chinese restaurant process

Arfa

Yusof

Shabanzadeh

2019

PeerJ Computer Science

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Trajectory clustering and path modelling are two core tasks in intelligent transport systems with a wide range of applications, from modeling drivers' behavior to traffic monitoring of road intersections. Traditional trajectory analysis considers them as separate tasks, where the system first clusters the trajectories into a known number of clusters and then the path taken in each cluster is modelled. However, such a hierarchy does not allow the knowledge of the path model to be used to improve the performance of trajectory clustering. Based on the distance dependent Chinese restaurant process (DDCRP), a trajectory analysis system that simultaneously performs trajectory clustering and path modelling was proposed. Unlike most traditional approaches where the number of clusters should be known, the proposed method decides the number of clusters automatically. The proposed algorithm was tested on two publicly available trajectory datasets, and the experimental results recorded better performance and considerable improvement in both datasets for the task of trajectory clustering compared to traditional approaches. The study proved that the proposed method is an appropriate candidate to be used for trajectory clustering and path modelling.

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Dynamic scene modelling and anomaly detection based on trajectory analysis

Cited by 10 publications

References 20 publications

Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering

Video‐based method for detecting potential risks among multiple electric bikes

Novel trajectory clustering method based on distance dependent Chinese restaurant process

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