Hierarchically linked infinite hidden Markov model based trajectory analysis and semantic region retrieval in a trajectory dataset

Kwon, Yongjin; Kang, Kyuchang; Jin, Jiahui; Moon, Jinyoung; Park, Jongyoul

doi:10.1016/j.eswa.2017.02.026

Cited by 14 publications

(2 citation statements)

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“…Supervised trajectory analysis Various form of supervised methods utilizes different trajectory features for learning. Hidden Markov model (HMM)-based learning method (Kwon et al 2017) is used to extract semantic region for trajectory analysis. Artificial neural network-based method such as convolutional neural network (CNN) (Mehrasa et al 2018) is used to analyse player trajectory and team activity and also in detecting, tracking, and traffic behaviour analysis (Ren et al 2018).…”

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confidence: 99%

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

et al. 2020

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Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead.

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Video trajectory analysis using unsupervised clustering and multi-criteria ranking

et al. 2020

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“…Second, the trajectories in the clusters are modeled using the posterior Bayesian probability for applications in anomaly detection and path prediction. In the study of [57], a hierarchical architecture on trajectory data is used for semantic region discovery. The authors adopt the concept of a hierarchically linked infinite hidden Markov model, which can capture the temporal dependency between adjacent observations detecting regions of the scene that have seman-2.…”

Section: Trajectory Based Analysismentioning

confidence: 99%

Human behavior understanding from motion and bodily cues using deep neural networks

Dotti

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Extraction of Long-Duration Moving Object Trajectories from Curtailed Tracks

Sekh

Dogra

Kar

et al. 2018

Proceedings of 2nd International Conference on Computer Vision &Amp; Image Processing

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Hierarchically linked infinite hidden Markov model based trajectory analysis and semantic region retrieval in a trajectory dataset

Cited by 14 publications

References 34 publications

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Video trajectory analysis using unsupervised clustering and multi-criteria ranking

Human behavior understanding from motion and bodily cues using deep neural networks

Extraction of Long-Duration Moving Object Trajectories from Curtailed Tracks

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