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Title: An integrated block based motion estimation framework for video applications
Author: Bhaskar, Harish
ISNI:       0000 0001 3463 1327
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2007
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Motion Estimation is a popular technique for computing the displacement vectors of objects or attributes between image frames at different time stamps. Motion estimation is critical and forms an integral part of many application domains such as video coding, compression, object tracking, video indexing, video stabilization, etc. However, the available motion models are restricted in their generality and have been tailored for use in specific application areas. The purpose of this thesis is to propose an integrated block-based motion estimation framework that serves different real-time video applications including; object tracking, video stabilization and low level video indexing. In this thesis, the proposed framework for motion estimation is based on block matching, a well known strategy for motion estimation particularly in the domain of video coding and compression. Traditional block matching techniques are limited in the following ways: i) block partitioning methods, whether fixed or variable sized, divide image frames into blocks blindly neglecting the features involved within. ii) block search schemes assume restricted translational displacernents (usually bounded by a search window: time complexity rises exponentially against search size). iii) during block matching, blocks undergo negligible or null rotntional motion and cannot suffer complex deformation characteristics during motion. iv) finally, block matching methods arc incapable of handling occlusion. The thesis will propose an Integrated block-based motion estimation framework that handles the aforementioned limitations of existing schemes. First, we propose a vector quantization based block partitioning methodology that will extend the quad-tree mechanism, but, place partitions such that similar or near-similar attributes are clustered within the same block. In this way we preserve the advantages of variable block matching by using a well defined quad-tree data structure and separate regions of interest from the rest of the image at the same time. At the second level of abstraction, we propose a genetic algorithm based block search scheine. A genetic algorithm based search mechanism will present a similar range of computation time irrespective of the amount of displacement. This will allow the search space to remain unrestricted and maintain tolerable time complexity. An immediate extension to the basic framework is presented as a rotation invariant scheme that is further generalized into a deformation handling mechanism for motion estimation. We use an affine based integrated model along-side the genetic algorithm search to match blocks (in turn image attributes) that may or may not undergo rotation or deformation during motion estimation. We also present a novel extension to the 2D affine genetic algorithm combination for handling specific 3D rotational changes to blocks. In this thesis, we also propose to integrate a novel motion correction mechanism based on probabilistic motion modeling for occlusion handling. Finally, for optimization purposes we combine scale space based architecture to the framework. This optimization procedure will allow the system to automatically choose, based on performance metrics, the operating resolution so that the quality to time ratio is maintained. We test the developed framework in different real-time applications. First, we present object tracking using block-based motion estimation. We prove through this research that more reliable object tracking results can be obtained by combining motion characteristics with feature tracking. Second, we present a video stabilization model using the proposed motion estimation technique. In this application, we cornpare the performance of the proposed block-based motion estimation scheme to the techniques specified in the literature and hence prove the efficiency and robustness of the framework. Finally, we tackle the problem of low level video indexing using a weighted combination of features during motion estimation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available