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Himomodel.si
Learning, analysis, and detection of motion in the framework of a hierarchical compositional visual architecture
Activity recognition results on UCF Sports and Holywood2
Table above shows the results, obtained on UCF Sports dataset (http://crcv.ucf.edu/data/UCF_Sports_Action.php). We report recognition rate with respect to the number...
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Computational efficiency and parallel implementation
The developed algorithms are computationally effective and the compositional processing pipeline is well-suited for implementation on massively parallel architectures. Many...
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Motion hierarchy structure
Our model is comprised of three processing stages, as shown in the Figure. The task of the lowest stage (layers...
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Server crash
After experiencing a total server failure, we are back online. We apologize for the inconvenience - we are still in...
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L1: motion features
Layer L1 provides an input to the compositional hierarchy. Motion, obtained in L0 is encoded using a small dictionary.
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Publication: ACCV 2012
Non-sequential multi-view detection, localization and identication of people using multi-modal feature maps
The paper describes a method for an off-line analysis of human motion using views from multiple cameras, which enables reliable localization and identification of persons without the possibility of cascading errors and total failure of the tracking. The method is based on fusion of multiple sources of visual information, where relatively basic and noisy low level features are extracted at the bottom of the hierarchy. They are processed in a manner which models increasingly complex interactions, resulting in significantly more reliable results. Acceptance rate for Asian Conference on Computer Vision in 2012: 26.8%
Read more...Publication: Evolutionary Computation
Asynchronous master-slave parallelization of differential evolution for multiobjective optimization
Paper describes a parallel asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multiobjective optimization. The implementation extends the use of DEMO from single processor use, to multiple interconnected multi-processor computers. It achieves high efficiency even on heterogeneous computer architectures. The paper describes a parallel algorithm, its differences from the serial algorithm and introduces a new measure of parallelism efficiency for the evolutionary algorithms. Evolutionary Computation impact factor: 1.061, ranking: 31/99.
Read more...Publication: EEE Transactions on Circuits and Systems for Video Technology
Efficient feature distribution for object matching in visual sensor networks
The article describes generic methodology for efficient distribution of features in visual sensor networks (e.g. a network of intelligent cameras), with special emphasis on the hierarchical structure of the features. The transmission of such hierarchically constructed features significantly decreases communication burden on the network, while retaining the performance of the nonhierarchically structured features. IEEE TCSVT Impact factor = 1.649, ranking 68/245.
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