BAYESIAN MULTIPLE PERSON TRACKING USING PROBABILITY HYPOTHESIS DENSITY SMOOTHING
نویسندگان
چکیده
منابع مشابه
Bayesian Multiple Person Tracking Using Probability Hypothesis Density Smoothing
We presents a PHD filtering approach to estimate the state of an unknown number of persons in a video sequence. Persons are represented by moving blobs, which are tracked across different frames using a first-order moment approximation to the posterior density. The PHD filter is a good alternative to standard multi-target tracking algorithms, since overrides making explicit associations INTERNA...
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The random-set framework for multiple target tracking offers a distinct alternative to the traditional approach to multiple target tracking by treating the collections of individual targets and observations as finite-sets. The multi-target state is predicted and updated recursively based on the set-valued observation. The complexity of computing the multi-target recursion grows exponentially wi...
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The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally impleme...
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The probability hypothesis density (PHD) filter is a recursive algorithm for solving multi-target tracking problems. The method consists of replacing the expectation of a random variable used in the traditional Bayesian filtering equations, by taking generalized expectations using the random sets formalism. In this approach, a set of observations is used to estimate the state of multiple and un...
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Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from d...
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ژورنال
عنوان ژورنال: International Journal on Smart Sensing and Intelligent Systems
سال: 2011
ISSN: 1178-5608
DOI: 10.21307/ijssis-2017-440