Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models
| Title: | Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models |
|---|---|
| Authors: | Sanjjamts, Amartaivan, Morita, Hiroshi, Enkhtogtokh, Togootogtokh |
| Publication Year: | 2025 |
| Collection: | Computer Science |
| Subject Terms: | Computer Science - Machine Learning |
| More Details: | Understanding collective pedestrian movement is crucial for applications in crowd management, autonomous navigation, and human-robot interaction. This paper investigates the use of sequential deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers, for real-time flock detection in multi-pedestrian trajectories. Our proposed approach consists of a two-stage process: first, a pre-trained binary classification model is used for pairwise trajectory classification, and second, the learned representations are applied to identify multi-agent flocks dynamically. We validate our method using real-world group movement datasets, demonstrating its robustness across varying sequence lengths and diverse movement patterns. Experimental results indicate that our model consistently detects pedestrian flocks with high accuracy and stability, even in dynamic and noisy environments. Furthermore, we extend our approach to identify other forms of collective motion, such as convoys and swarms, paving the way for more comprehensive multi-agent behavior analysis. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2502.15252 |
| Accession Number: | edsarx.2502.15252 |
| Database: | arXiv |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sanjjamts%2C+Amartaivan%22">Sanjjamts, Amartaivan</searchLink><br /><searchLink fieldCode="AR" term="%22Morita%2C+Hiroshi%22">Morita, Hiroshi</searchLink><br /><searchLink fieldCode="AR" term="%22Enkhtogtokh%2C+Togootogtokh%22">Enkhtogtokh, Togootogtokh</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: Understanding collective pedestrian movement is crucial for applications in crowd management, autonomous navigation, and human-robot interaction. This paper investigates the use of sequential deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers, for real-time flock detection in multi-pedestrian trajectories. Our proposed approach consists of a two-stage process: first, a pre-trained binary classification model is used for pairwise trajectory classification, and second, the learned representations are applied to identify multi-agent flocks dynamically. We validate our method using real-world group movement datasets, demonstrating its robustness across varying sequence lengths and diverse movement patterns. Experimental results indicate that our model consistently detects pedestrian flocks with high accuracy and stability, even in dynamic and noisy environments. Furthermore, we extend our approach to identify other forms of collective motion, such as convoys and swarms, paving the way for more comprehensive multi-agent behavior analysis. – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2502.15252" linkWindow="_blank">http://arxiv.org/abs/2502.15252</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2502.15252 |
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| RecordInfo | BibRecord: BibEntity: Subjects: – SubjectFull: Computer Science - Machine Learning Type: general Titles: – TitleFull: Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sanjjamts, Amartaivan – PersonEntity: Name: NameFull: Morita, Hiroshi – PersonEntity: Name: NameFull: Enkhtogtokh, Togootogtokh IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 02 Type: published Y: 2025 |
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