Unsupervised Tracking With the Doubly Stochastic Dirichlet Process Mixture Model

We present an unsupervised tracking algorithm for human and car trajectory detection, using what is called the temporal doubly stochastic Dirichlet process (TDSDP) mixture model. The TDSDP captures the global dependence and the variation of human crowds and cars in temporal domains without the Marko...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2016-09, Vol.17 (9), p.2594-2599
Main Authors: Xing Sun, Yung, Nelson H. C., Lam, Edmund Y.
Format: Article
Language:English
Subjects:
Algorithms
Computational modeling
Dirichlet problem
doubly stochastic
Feature extraction
Gaussian process
Hidden Markov models
Inference
Markov analysis
Markov processes
Mixture models
Monte Carlo methods
Monte Carlo simulation
Nonparametric Bayesian
Sampling
Stochasticity
Tracking
Trajectories
Trajectory
unsupervised tracking
Vehicles
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Summary:We present an unsupervised tracking algorithm for human and car trajectory detection, using what is called the temporal doubly stochastic Dirichlet process (TDSDP) mixture model. The TDSDP captures the global dependence and the variation of human crowds and cars in temporal domains without the Markov assumption, making it particularly suitable for long-term tracking. Moreover, TDSDP prior can estimate the number of trajectories automatically. We first define the TDSDP based on the Cox process and then explain how to construct a TDSDP mixture model from thinning multiple Dirichlet process mixtures (DPMs) with conjugate priors. Next, a Markov chain Monte Carlo sampling inference is presented. Experimental results on synthetic and real-world data demonstrate that the proposed TDSDP mixture is superior to the DPM and the dependent Dirichlet process (DDP) in terms of topic variation modeling. PETS2001 data set experiments show that TDSDP has more robust object tracking capability over DDP based on generalized Polya urn. Low-quality fish data set experiments indicate that the TDSDP excels at solving tracking problems with insufficient features.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2518212