Abnormal Events Detection Based on Trajectory Clustering

Ghrab, Najla Bouarada; Fendri, Emna; Hammami, Mohamed

doi:10.1109/cgiv.2016.65

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…For a trajectory R, let

be the trajectory

. LCSS of R and S trajectories is defined in the following equation [ 29 ].

where

and

are pre-defined parameters that depend on the application and the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…The clusters that have only a trajectory are finally marked as anomalies. Unlike in [ 3 ], the authors in [ 29 ] developed a two-phase anomalous trajectory detection framework: Online phase and offline phase. The offline phase finds the clusters of trajectories.…”

Section: Related Workmentioning

confidence: 99%

“…Hierarchical clustering. The hierarchical clustering-based anomaly detection method is proposed in [ 29 ]. This work detects abnormal trajectories based on their closeness with extracted clusters from datasets.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Spectral clustering. From a previous study [ 29 ], the hierarchical clustering algorithm is replaced with the spectral clustering algorithm for finding clusters from datasets. Anomalies are found in the same way in [ 29 ].…”

Section: Performance Evaluationmentioning

confidence: 99%

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Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Lan

Yoon

2023

Sensors

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Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories, extending from the longest common sub-sequence (LCSS). Moreover, a DBSCAN cluster validity index (DCVI) is proposed to improve the trajectory clustering performance. The DCVI is used to choose the epsilon parameter for DBSCAN. The proposed method is evaluated using two real trajectory datasets: MIT Badge and sCREEN. The experimental results show that the proposed method effectively detects human trajectory anomalies in indoor spaces. With the MIT Badge dataset, the proposed method achieves 89.03% in terms of F1-score for hypothesized anomalies and above 93% for all synthesized anomalies. In the sCREEN dataset, the proposed method also achieves impressive results in F1-score on synthesized anomalies: 89.92% for rare location visit anomalies (τ = 0.5) and 93.63% for other anomalies.

show abstract

“…For a trajectory R, let

be the trajectory

. LCSS of R and S trajectories is defined in the following equation [ 29 ].

where

and

are pre-defined parameters that depend on the application and the dataset.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Lan

Yoon

2023

Sensors

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show abstract

“…Ghrab et al [6] used trajectory-based feature descriptors and performed hierarchical clustering to remove noise from the training data. Kaltsa et al [13] extracted spatiotemporal cubes and obtained a merged feature vector comprising of Histograms of Oriented Gradients (HOG) for the spatial information and Histogram of Oriented Swarm Acceleration (HOSA) to capture temporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

Residual spatiotemporal autoencoder for unsupervised video anomaly detection

Kumar

Chandrakala

Mohan

2020

SIViP

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Modeling abnormal spatiotemporal events is challenging since data belonging to abnormal activities are less in the course of a surveillance stream. We solve this issue using a normality modeling approach, where abnormalities are detected as deviations from the normal patterns. To this end, we propose a residual spatiotemporal autoencoder, which is trainable end-to-end to carry out the anomaly detection task in surveillance videos. Irregularities are detected using the reconstruction loss, where normal frames are reconstructed well with a low reconstruction cost, and the converse is identified as abnormal frames. We evaluate the effect of residual connections in the STAE architecture and presented good practices to train an autoencoder for video anomaly detection using benchmark datasets, namely CUHK-Avenue, UCSD-Ped2, and Live Videos. Comparisons with the existing approaches prove that the effectiveness of residual blocks is incremental than going deeper with additional layers to train a spatiotemporal autoencoder with good generalization across datasets.

show abstract

Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

Almazroey

Jarraya

2020

Advances in Intelligent Systems and Computing

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Abnormal Events Detection Based on Trajectory Clustering

Cited by 10 publications

References 22 publications

Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Residual spatiotemporal autoencoder for unsupervised video anomaly detection

Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection

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