Journal ArticleDOI
Granular Flow Graph, Adaptive Rule Generation and Tracking
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TLDR
A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking, and it is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation.Abstract:
A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.read more
Citations
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Boosted K-nearest neighbor classifiers based on fuzzy granules
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Granulated deep learning and Z-numbers in motion detection and object recognition
TL;DR: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers and shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition.
References
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Journal ArticleDOI
Neighborhood granules and rough rule-base in tracking
TL;DR: A new concept of neighborhood granule formation over images is introduced here which proves to be effective in reducing the indiscernibility of the rule-base and provides more accurate results in the task of tracking.
Book ChapterDOI
Modelling Object Behaviour in a Video Surveillance System Using Pawlak’s Flowgraph
Karol Lisowski,Andrzej Czyzewski +1 more
TL;DR: The Pawlak’s flowgraph is used as a container for the knowledge related to the behaviour of objects in the area supervised by a video surveillance system and an adaptive algorithm is implemented.