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Online ISSN 1751-9640 Print ISSN 1751-9632

IET Computer Vision

Volume 6, Issue 1, January 2012

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Volume 6, Issue 1

January 2012

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    • Adaptive mean-shift for automated multi object tracking
      • Author(s): C. Beyan  and  A. Temizel
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 1 –12
      • DOI:  10.1049/iet-cvi.2011.0054
      • Type: Article
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      • p. 1 –12 (12)
        Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift.
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      Multiview geometry in traditional vision and omnidirectional vision under the L∞-norm
      • Author(s): L. Zhang ; Y. Hu ; J. Zhang ; Y. Li
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 13 –20
      • DOI:  10.1049/iet-cvi.2010.0111
      • Type: Article
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      • p. 13 –20 (8)
        This study presents a review of multiview geometry problems in traditional vision and omnidirectional vision under the L∞-norm. The main advantage of this approach is a theoretical guarantee of global optimality. First, three core problems in multiview geometry in traditional vision are formulated as second-order cone programming feasibility problems. The extension of L∞-norm approach for multiview geometry from traditional vision to omnidirectional vision is shown by three models, a mirror model, a sphere model and a cylinder model. Finally, the authors assess their potential for future deployment and present directions for future research.
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      Hierarchical stochastic fast search motion estimation algorithm
      • Author(s): S. Tedmori  and  N. Al-Najdawi
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 21 –28
      • DOI:  10.1049/iet-cvi.2010.0188
      • Type: Article
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      • p. 21 –28 (8)
        Many fast search motion estimation algorithms have been developed to reduce the computational cost required by full-search algorithms. Fast search motion estimation techniques often converge to a local minimum, providing a significant reduction in computational cost. The motion vector measurement process in fast search algorithms is subject to noise and matching errors. Therefore researchers have investigated the use of Kalman filtering in order to seek optimal estimates. In this work, the authors propose a new fast stochastic motion estimation technique that requires 5% of the total computations required by the full-search algorithm, and results in a quality that outperforms most of the well-known fast searching algorithms. The measured motion vectors are obtained using a simplified hierarchical search block-matching algorithm, and are used as the measurement part of the Kalman filter. As for the prediction part of the filter, it is assumed that the motion vector of a current block can be predicted from its four neighbouring blocks. Using the predicted and measured motion vectors, the best estimates for motion vectors are obtained. Using standard methods of accuracy measurements, results show that the performance of the proposed technique approaches that of the full-search algorithm.
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      Depth measurement using single camera with fixed camera parameters
      • Author(s): Y. Wei ; Z. Dong ; C. Wu
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 29 –39
      • DOI:  10.1049/iet-cvi.2010.0017
      • Type: Article
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      • p. 29 –39 (11)
        Owing to the space limitation and the strict requirement on operation, depth measurement using single visual sensor is necessary in many applications, such as mini-robot, precision processing and micro/nano-manipulation. Depth from defocus (DFD), a typical method applied in depth reconstruction, has been extensively researched and has developed greatly in recent years. However, all the existing DFD algorithms has focused only on the situation that blurring images with different camera parameters (i.e. focal length or radius of the lens), and it resulted in the inapplicability of these algorithms in cases where any change of camera parameters is absolutely forbidden. Therefore a novel DFD method considering different images with fixed camera parameters is given. First, the blurring imaging model is constructed with the relative blurring and the diffusion equation. Secondly the relation between depth and blurring is discussed. Subsequently, the depth measurement problem is transformed into an optimisation issue. Finally, simulations and experiments are conducted to show the feasibility and effectiveness of the proposed method.
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      Vanishing point detection in corridors: using Hough transform and K-means clustering
      • Author(s): R. Ebrahimpour ; R. Rasoolinezhad ; Z. Hajiabolhasani ; M. Ebrahimi
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 40 –51
      • DOI:  10.1049/iet-cvi.2010.0046
      • Type: Article
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      • p. 40 –51 (12)
        One of the main challenges in steering a vehicle or a robot is the detection of appropriate heading. Many solutions have been proposed during the past few decades to overcome the difficulties of intelligent navigation platforms. In this study, the authors try to introduce a new procedure for finding the vanishing point based on the visual information and K-Means clustering. Unlike other solutions the authors do not need to find the intersection of lines to extract the vanishing point. This has reduced the complexity and the processing time of our algorithm to a large extent. The authors have imported the minimum possible information to the Hough space by using only two pixels (the points) of each line (start point and end point) instead of hundreds of pixels that form a line. This has reduced the mathematical complexity of our algorithm while maintaining very efficient functioning. The most important and unique characteristic of our algorithm is the usage of processed data for other important tasks in navigation such as mapping and localisation.
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      Scale and orientation adaptive mean shift tracking
      • Author(s): J. Ning ; L. Zhang ; D. Zhang ; C. Wu
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 52 –61
      • DOI:  10.1049/iet-cvi.2010.0112
      • Type: Article
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      • p. 52 –61 (10)
        A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this study to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, whereas the scale and orientation changes cannot be adaptively estimated. Considering that the weight image derived from the target model and the candidate model can represent the possibility that a pixel belongs to the target, the authors show that the original mean shift tracking algorithm can be derived using the zeroth- and the first-order moments of the weight image. With the zeroth-order moment and the Bhattacharyya coefficient between the target model and candidate model, a simple and effective method is proposed to estimate the scale of target. Then an approach, which utilises the estimated area and the second-order centre moment, is proposed to adaptively estimate the width, height and orientation changes of the target. Extensive experiments are performed to testify the proposed method and validate its robustness to the scale and orientation changes of the target.
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      Robust mean-shift tracking with corrected background-weighted histogram
      • Author(s): J. Ning ; L. Zhang ; D. Zhang ; C. Wu
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 62 –69
      • DOI:  10.1049/iet-cvi.2009.0075
      • Type: Article
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      • p. 62 –69 (8)
        The background-weighted histogram (BWH) algorithm proposed by Comaniciu et al. attempts to reduce the interference of background in target localisation in mean-shift tracking. However, the authors prove that the weights assigned to pixels in the target candidate region by BWH are proportional to those without background information, that is, BWH does not introduce any new information because the mean-shift iteration formula is invariant to the scale transformation of weights. Then a corrected BWH (CBWH) formula is proposed by transforming only the target model but not the target candidate model. The CBWH scheme can effectively reduce background's interference in target localisation. The experimental results show that CBWH can lead to faster convergence and more accurate localisation than the usual target representation in mean-shift tracking. Even if the target is not well initialised, the proposed algorithm can still robustly track the object, which is hard to achieve by the conventional target representation.
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      Evaluation of two-part algorithms for objects’ depth estimation
      • Author(s): R. Kouskouridas ; A. Gasteratos ; E. Badekas
      • Source: IET Computer Vision, Volume 6, Issue 1, p. 70 –78
      • DOI:  10.1049/iet-cvi.2009.0094
      • Type: Article
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      • p. 70 –78 (9)
        During the last decade, a wealth of research was devoted to building integrated vision systems capable of both recognising objects and providing their spatial information. Object recognition and pose estimation are among the most popular and challenging tasks in computer vision. Towards this end, in this work the authors propose a novel algorithm for objects’ depth estimation. Moreover, they comparatively study two common two-part approaches, namely the scale invariant feature transform SIFT and the speeded-up robust features algorithm, in the particular application of location assignment of an object in a scene relatively to the camera, based on the proposed algorithm. Experimental results prove the authors' claim that an accurate estimation of objects' depth in a scene can be obtained by taking into account extracted features’ distribution over the target's surface.
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