REU 2013 Report 3Alla Petrakova

Last week recap

Trajectory Clustering TRACLUS UCF Motion Pattern Algorithm

Quality of ClustersAttempt to find a Generally Accepted Quantiative Measure

Approaches to Evaluating Quality of Clusters

QUALITATIVE

Ground truth Visual inspection Synthetic datasets Comparison to

another algorithm

QUANTITATIVE

Correct Clustering Rate

Sum of Squared Error Accuracy Measure

± Error or Noise Penalty

SSE

J. gil Lee and J. Han. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM International Conference on Management of Data (SIGMOD), Beijing, China, pages 593–604, 2007. Cited by 357

Sum of Squared Error

N denotes the set of all noise line segments.

Correct Clustering Rate

B. Morris and M. Trivedi, “Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 312- 319, June 2009.

Correct Clustering Rate

Find one-to-one mapping between the ground truth and clustering labels which maximized the number of matches.

where N is the total number of trajectories and pc denotes the total number of trajectories matched to the c-th cluster.

Accuracy Measure

IN – total number of clusters bi = the number of labeled

trajectories that are most frequent in a given cluster

Bi = the total number of trajectories in a cluster

Testing

Vehicle Motion Patterns

Dataset:

TRACLUS results

UCF Motion Pattern results

Australian Sign Language Dataset

Used in Following Papers:

M. Vlachos, G. Kollios, and D. Gunopulos, “Discovering Similar Multidimensional Trajectories,” Proc. Int’l Conf. Data Eng., pp. 673- 684, 2002. (cited by 631)

Lei Chen, M. Tamer Özsu, and Vincent Oria. 2005. Robust and fast similarity search for moving object trajectories. In Proc. of the 2005 ACM SIGMOD int’l conf. on Management of data (SIGMOD '05). ACM, New York, NY, USA, 491-502. DOI=10.1145/1066157.1066213 (Cited by 395)

A. Naftel and S. Khalid, “Motion Trajectory Learning in the DFT- Coefficient Feature Space,” Proc. IEEE Int’l Conf. Computer Vision Systems, pp. 47-47, Jan. 2006. (cited by 26)

W. Hu, X. Li, G. Tian, S. Maybank, and Z. Zhang, ” An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 35, NO. 5, MAY 2013

Tsumoto, S., Hirano, S.: Detection of risk factors using trajectory mining. J. Intell. Inf. Syst. 36(3), 403–425 (2011) (cited by 15)

Australian Sign Language Dataset

ASL Testing

No meaningful results Separating out individual trajectories