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Title: Trajectory-Based Scene Understanding Using Dirichlet Process Mixture Model
Authors: Santhosh K.K.
Dogra D.P.
Pratim Roy, Partha
Chaudhuri B.B.
Published in: IEEE Transactions on Cybernetics
Abstract: Appropriate modeling of a surveillance scene is essential for the detection of anomalies in road traffic. Learning usual paths can provide valuable insight into road traffic conditions and thus can help in identifying unusual routes taken by commuters/vehicles. If usual traffic paths are learned in a nonparametric way, manual interventions in road marking can be avoided. In this paper, we propose an unsupervised and nonparametric method to learn the frequently used paths from the tracks of moving objects in \Theta (kn) time, where k denotes the number of paths and n represents the number of tracks. In the proposed method, temporal dependencies of the moving objects are considered to make the clustering meaningful using temporally incremental gravity model (TIGM). In addition, the distance-based scene learning makes it intuitive to estimate the model parameters. Further, we have extended the TIGM hierarchically as a dynamically evolving model (DEM) to represent notable traffic dynamics of a scene. The experimental validation reveals that the proposed method can learn a scene quickly without prior knowledge about the number of paths ( k ). We have compared the results with various state-of-the-art methods. We have also highlighted the advantages of the proposed method over the existing techniques popularly used for designing traffic monitoring applications. It can be used for administrative decision making to control traffic at junctions or crowded places and generate alarm signals, if necessary. © 2013 IEEE.
Citation: IEEE Transactions on Cybernetics, 51(8): 4148-4161
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Bayesian inference
Dirichlet process mixture model (DPMM)
Gibbs sampling
incremental trajectory clustering
intelligent transportation system
nonparametric model
unsupervised learning
ISSN: 21682267
Author Scopus IDs: 56525012800
Author Affiliations: Santhosh, K.K., School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
Dogra, D.P., School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
Chaudhuri, B.B., Techno India University, Kolkata, India
Corresponding Author: Dogra, D.P.; School of Electrical Sciences, India; email:
Appears in Collections:Journal Publications [CS]

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