The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning trajectory patterns by clustering: Experimental studies and comparative evaluation
Abstract
Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.
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Cites
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Cited by
A survey on motion prediction and risk assessment for intelligent vehicles
Lefèvre, S., Vasquez, D., & Laugier, C. (2014) · ROBOMECH Journal · doi:10.1186/s40648-014-0001-z · Cited by 1,112
Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.09.002 · Cited by 371
Morris, B. T., & Trivedi, M. M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.64 · Cited by 349
Self-citationUncovering Trajectories of Informal Learning in Large Online Communities of Creators
Yang, S., Domeniconi, C., Revelle, M., Sweeney, M., Gelman, B. U., Beckley, C., & Johri, A. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724674 · Cited by 60
Metadata
- Authors
- Morris, B., & Trivedi, M.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 312-319
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2163145488
- Cited by
- 228
- References
- 1
- FWCI
- 7.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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