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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15837
Title: Local behavior analysis for trajectory classification using graph embedding
Authors: Saini R.
Kumar P.
Dutta S.
Pratim Roy, Partha
Pal U.
Published in: Proceedings of 4th Asian Conference on Pattern Recognition, ACPR 2017
Abstract: Understanding motion patterns is of great importance to analyze the behavior of objects in the vigilance area. Grouping the motion patterns into clusters in such a way that similar motion patterns lie in same cluster and the inter-cluster variance is maximized. Variation in the duration of trajectory patterns in terms of time or number of points in them (even in the trajectories from same cluster) make it more difficult to correctly classify in respective clusters as a bijective mapping is not possible in such cases. In this paper, we have formulated the trajectory classification problem into graph based similarity problem using Douglas-Peucker (DP) algorithm and complete bipartite graphs. Local behavior of objects has been analyzed using their motion segments and Dynamic Time Warping (DTW) has been used for finding similarity among motion trajectories. Class-wise global and local costs have been computed using DTW and their fusion has been done using Particle Swarm Optimization (PSO) to improve the classification rate. Experiments have been performed using two public trajectory datasets, namely T15 and LabOmni. The proposed method yields encouraging results and outperforms the state of the art techniques. © 2017 IEEE.
Citation: Proceedings of 4th Asian Conference on Pattern Recognition, ACPR 2017, (2018), 411- 416
URI: https://doi.org/10.1109/ACPR.2017.27
http://repository.iitr.ac.in/handle/123456789/15837
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Bipartite Graph
Classification and Clustering
Dynamic Time Warping
Graph Embedding
Local Behavior
Trajectory
ISBN: 9.78154E+12
Author Scopus IDs: 57190288840
57212043589
57194562842
56880478500
57200742116
Author Affiliations: Saini, R., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Kumar, P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Dutta, S., Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Pal, U., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
Appears in Collections:Conference Publications [CS]

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