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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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Problem identification - Problems

To successfully apply such framework to motion perception, several problems need to be tackled.

Hierarchical structure

Structure of the hierarchical compositional model itself needs to be devised. The following research questions have to be addressed.

  1. What is the most appropriate basic design for the initial elementary detectors on the lowest level of the hierarchy?
  2. How to map spatial and temporal motion aspects into a hierarchical spatio­temporal structure?
  3. How to introduce spatial and temporal scale in the hierarchical structure?
  4. How to achieve an appropriate invariance and abstraction on each level of the hierarchy?
  5. How to learn the connections between the elements of the hierarchy and how to perform efficient inference?

Parameters

The parameters will be obtained by testing the model with real­world visual data, obtaining the model output, and examining the output for conformance with the statistics of natural images. Among others, the dimensionality and granularity of the motion hierarchy will be established.

Validation and adjustment

The primary goal of our research is a generative model. Nevertheless, for use in real 
computer vision problems, the model will have to exhibit appropriate discriminative 
properties as well. For this purpose the model with have to be evaluated on a real world 
data, and compared to state­of­the­art results. Based on those results, the model will be 
refined and it structure re­examined. The model will be also evaluated in terms of storage 
efficiency, that means that it will be able to store the learned information in a much more 
efficient way, than is currently possible with the state­of­the­art methods, at the 
comparable recognition accuracy. Other performance evaluation criteria will be the efficiency 
of inference and learning, transfer of knowledge, and generalisation capabilities.

Integration with shape­based hierarchical compositionality model

In biological systems, motion is only one of the cues, which are exploited for observation and understanding of one’s environment. It is known that motion pathway is interconnected with the pathway processing shape [Beck2010]. Therefore we will also look at computational reasons and advantages for combining shape and motion hierarchies. We expect that the newly developed hierarchical compositional motion model will be integrated with existing shape­based hierarchical compositionality model. The nature and the appropriate extent of communication between the two models will be studied with further testing and adaptation of both models. The end result of such integration will be the composite model, which will be able to use both shape and motion information to understand real world environment.

Efficient parallel implementation

To enable productive pace of development, parallel versions of algorithms will be developed through several phases of the project. This will allow real­time or near real­time implementations of the low level algorithms on the state­of­the­art, massively parallel architectures such as Nvidia CUDA, and significantly improved performance of the higher level algorithms on the modern multi­core processors. It is expected that the developed algorithms could be ported to parallel architectures with relative ease, since the modern massively parallel architectures correspond well to the lower levels of the human visual perceptive system, and the hierarchical compositional structure offers ample opportunities for task parallelization on the higher levels of the structure.

Consortium

In line with the required competence of the project partners, we have composed our consortium from four groups: University of Ljubljana, Faculty of Computer and Information Science, Visual Cognitive Systems Laboratory (FRI VICOS), and Faculty of Electrical Engineering Machine Vision Laboratory (FE MVL), Jožef Stefan Institute, Department of Automatics, Biocybernetics, and Robotics (IJS DABR) and Department of Communication Systems (IJS DCS), each of which specializes in a particular topic that will be indispensable for achieving the project’s goal. The VICOS group has recently successfully developed a methodology for automatic construction (learning) and inference in compositional models for shape categorization. The MVL group has a long history of research in motion tracking and analysis. The expertise of VICOS and MVL will play a central role to design the methodology of hierarchical compositional models of motion. The DCS group specializes in parallel algorithms and parallel architectures and will contribute to fast implementation of our algorithms. The scientific focus of the DABR group is cognitive robotics and will provide an experimental platform for evaluation of the implementations.

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