Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot

Karabiber, Fethullah; Arena, Paolo; Fortuna, Luigi; Fiore, S. De; Vagliasindi, G.; Arik, Sabri

doi:10.1007/s11265-010-0504-7

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Our plan is to implement the segmentation algorithm developed herein on the Eye-RIS vision system in the near future. To this purpose, note that one of the authors (F. Karabiber) has already started to work on the Eye-RIS vision system, as is proof by the results published in [22].…”

Section: Discussionmentioning

confidence: 84%

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

Karabiber

Vecchio

Grassi

2011

EURASIP J. Adv. Signal Process.

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The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensingprocessing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy.

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Section: Discussionmentioning

confidence: 84%

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

Karabiber

Vecchio

Grassi

2011

EURASIP J. Adv. Signal Process.

Self Cite

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“…Firstly it provides the capability within a single design for efficiently implementing a wide range of filter types and secondly it offers a further cost reduction via resource sharing for implementing those image-processing applications which require multiple types of filters sequentially for performing diversified image processing tasks. The diversified filter requirement for different applications ranges from biomedical [4, 5, 22, 23], computer vision [6, 24], surveillance and navigation [7, 25], industrial [26, 27] to geophysics [8] etc. In contrast to our proposed versatile framework, the previously reported structurally optimized designs such as [18] and [19] does not offer further cost reduction for such applications.…”

Section: Discussionmentioning

confidence: 99%

“…The [8] requires three identity quadrant symmetric filter, six anti-quadrant symmetric filters and six non-quadrant symmetric filters. The [25] and [24] require an identity quadrant symmetric filter, two anti-quadrant symmetric filters and two non-quadrant symmetric filters. Fig 11a shows that for implementing parallel filtering architectures of these applications, the multipliers cost increase linearly with increasing number of filters of complete designs by using any of the three different filter design approaches.…”

Section: Discussionmentioning

confidence: 99%

A Low Cost Structurally Optimized Design for Diverse Filter Types

et al. 2016

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A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image processing applications especially in a constraint environment.

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“…Furthermore, morphological operations can be used to make the extracted feature describe the target more accurately and remove noise as much as possible. 20,21 In this paper, we use a particular edge extraction method which combines morphology gradient, opening and closing operations. 22 By setting proper size of morphological kernels, the extraction method can make the edge information smoother and apparent than that obtained by classical edge operators such as sobel.…”

Section: Introductionmentioning

confidence: 99%

A self-adaptive edge matching method based on mean shift and its application in video tracking

Liu

Zhou

2013

The Imaging Science Journal

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A self-adaptive edge matching method based on mean shift adjustment is proposed in this paper. Such method uses the local mode seeking character of mean shift to adjust the edge information of each model to a stable state before matching, which can effectively avoid the deviation problem of traditional method and raise the successful matching rate. Furthermore, the interfering vector with a self-adaptive coefficient is proposed to optimise the matching performance in complex background. Compared with a pre-set constant coefficient, the self-adapted coefficient has a better perception of background edge complexity so as to control the initial adjusting position more rationally, and thus increases the robustness and accuracy of matching. This matching method is applied in an improved particle filtering tracking framework, and experimental results prove the validity and rationality of the theoretical analysis, and show that the proposed matching method performs a robust and efficient tracking.

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Implementation of a Moving Target Tracking Algorithm Using Eye-RIS Vision System on a Mobile Robot

Cited by 4 publications

References 9 publications

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

A Low Cost Structurally Optimized Design for Diverse Filter Types

A self-adaptive edge matching method based on mean shift and its application in video tracking

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