Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

Yao, Rui; Zhang, Yanning; Zhou, Yong; Xia, Shixiong

doi:10.1016/j.ijleo.2013.10.108

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A Review Of Most Important Applied Methods1

Introduction1

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Cited by 4 publications

(2 citation statements)

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“…Size variations of mobile objects over time are also good criteria to distinguish mobile objects from each other. Shadow features are proposed in bright environments [17][18][19][20].…”

Section: A Review Of Most Important Applied Methodsmentioning

confidence: 99%

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

Mazinan

2014

Optik

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“…Size variations of mobile objects over time are also good criteria to distinguish mobile objects from each other. Shadow features are proposed in bright environments [17][18][19][20].…”

Section: A Review Of Most Important Applied Methodsmentioning

confidence: 99%

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

Mazinan

2014

Optik

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“…In fact, the occlusion handling approach is realized to show the merits of PCA to avoid false updates. Yao et al (2014) presented tracking of multiple small objects based on dynamic Bayesian networks. This research proposes an endto-end approach for the tracking of multiple small objects via a combination of Gaussian mixture along with the tracking of dynamic Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

An intelligent estimation framework in the area of real-time tracking

Kazemi

Olamaei

2015

Transactions of the Institute of Measurement and Control

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A number of referenced materials in the area of real-time tracking have been considered to present a novel intelligent estimation framework (IBEF). The main idea behind the research is to realize an approach that is applicable and efficient with respect to the final outcomes in the same way. The IBEF is realized, as a decision maker, in association with the neural network, where all the types of chosen objects, such as military, private vehicles, animals and other related objects, may be tracked in a set of frames of video through their approach. Then, the IBEF in each frame should be corrected to reorganize the estimation, in a constructive manner. The main goals may be chosen as solid, non-solid, stationary and non-stationary objects, as long as all of them can be taken in different quantities. The applicability of the approach can be presented in the areas of flight control, collision avoidance, etc. The process of realizing the approach can be continued by extracting some prominent features regarding the tracked objects, in order to apply them to the IBEF. The desirable performance of the proposed approach is guaranteed though a standard scenario. The results are finally compared with a benchmark approach, where the improvement of the approach performance can be verified.

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Soft Computing based object detection and tracking approaches: State-of-the-Art survey

Kaushal

Khehra²,

Sharma

2018

Applied Soft Computing

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Multiple small objects tracking based on dynamic Bayesian networks with spatial prior

Cited by 4 publications

References 16 publications

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

A powerful approach to real-time mobile objects tracking in crowded environments utilizing 3D video analysis of fixed camera, particle filter and neural network

An intelligent estimation framework in the area of real-time tracking

Soft Computing based object detection and tracking approaches: State-of-the-Art survey

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