Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Lan, Doi Thi; Yoon, Seokhoon

doi:10.3390/s23063318

Cited by 7 publications

(2 citation statements)

References 44 publications

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“…Moreover, the spatial contexts of human activities, whether indoors or outdoors, introduce additional complexities. Additionally, relying solely on a single distance metric (e.g., the Euclidean distance) for cluster quality assessments may not be the most effective approach [ 26 ]. Therefore, efficient methods for selecting the appropriate number of clusters are necessary.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores—such as the silhouette coefficient, Dunn index, Calinski–Harabasz index, Davies–Bouldin index, and DS—for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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“…Common methods for dealing with anomaly detection tasks [5][6][7] can be generally classified as traditional machine learning methods and deep learning methods. SVM [8], One-class SVM (OC-SVM) [9], Isolation forest [10] and Local Outlier Factor (LOF) [11] are typical examples of machine learning anomaly detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

TCF-Trans: Temporal Context Fusion Transformer for Anomaly Detection in Time Series

Peng,

Li,

Lin

et al. 2023

Sensors

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Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection algorithm requires excellent learning ability of the data features. Transformers, which apply the self-attention mechanism, have shown outstanding performances in modelling long-range dependencies. Although Transformer based models have good prediction performance, they may be influenced by noise and ignore some unusual details, which are significant for anomaly detection. In this paper, a novel temporal context fusion framework: Temporal Context Fusion Transformer (TCF-Trans), is proposed for anomaly detection tasks with applications to time series. The original feature transmitting structure in the decoder of Informer is replaced with the proposed feature fusion decoder to fully utilise the features extracted from shallow and deep decoder layers. This strategy prevents the decoder from missing unusual anomaly details while maintaining robustness from noises inside the data. Besides, we propose the temporal context fusion module to adaptively fuse the generated auxiliary predictions. Extensive experiments on public and collected transportation datasets validate that the proposed framework is effective for anomaly detection in time series. Additionally, the ablation study and a series of parameter sensitivity experiments show that the proposed method maintains high performance under various experimental settings.

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TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

Wang

Shao

et al. 2023

Future Generation Computer Systems

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Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement

Cited by 7 publications

References 44 publications

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

TCF-Trans: Temporal Context Fusion Transformer for Anomaly Detection in Time Series

TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

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