A Review on Selected Target Tracking Algorithms

Kaawaase, Kyanda Swaib; Chi, Fu-Hsaing; Jiao, Shuhong; Ji, Qingge

doi:10.3923/itj.2011.691.702

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…In the paper [3], it is proved that the solutions of inhomogeneous KdV equation helps to get the right information about moving targets by using soliton resonance method. A novel neural architecture named "Spectral network" is being proposed for detecting targets in a cluttered background and results can be interpreted in terms of resonances by KdV equations [2]. Therefore we observe the importance of KdV equation in target tracking process which is important for sensor data analysis and leads to sustainability.…”

Section: Introductionmentioning

confidence: 92%

“…Therefore in recent years, researchers are working to find accurate and fast method to track real-world position and orientation of moving targets. Tracking is a process of estimating the current and future state of target [2]. Target tracking process can be defined as a set of algorithm and the algorithm is based on a nonlinear KdV equation as a moving target detector.…”

Section: Introductionmentioning

confidence: 99%

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Importance of $p-KdV$ equation in target tracking and its applications

Kulkarni¹,

Takale²,

Gaikwad³

2021

Malaya J. Mat.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Importance of $p-KdV$ equation in target tracking and its applications

Kulkarni¹,

Takale²,

Gaikwad³

2021

Malaya J. Mat.

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“…Wiener filter is designed by minimizing the MSE between the filtered image and the original image [17] . It can be applied to the image adaptively, tailoring itself to the local Image variance.…”

Section: Image Filtering Using Wiener Filtermentioning

confidence: 99%

Vision enhancement of line following robot using Wiener filter and morphology

2011

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Line following robot (LFR) plays an important rule in the recent industries, where it can carriers the products from one manufacturing plant to another which are usually in different buildings or separate blocks. This paper presents preliminary development of the LFR which use a normal laptop with a web cam to guide itself through a track. The designed LFR is insensitive to environmental factors such as darkness, lighting, camera distortion or line color. This development is based on computer vision enhancement using digital image processing. It is accomplished through the following stages: Firstly, the acquired RGB image using the web cam is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using adaptive histogram equalization, and then the image is filtered using wiener filter. Finally morphological operations are carried out. The results are evaluated qualitatively and quantitatively from the points of peak signal-to-noise ratio (PSNR), entropy, and image smoothness. The results show that the proposed method is promising for vision enhancement of LFR.

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“…The two feature fusion can compensate for the corresponding drawbacks. Through these two feature fusions, the STAPLE algorithm has a very good tracking accuracy [17][18][19][20][21][22][23][24]. However, the conventional STAPLE algorithm couldn't get the optimal fusion results because it adopts constant coefficients to perform the two-feature fusion.…”

Section: Introductionmentioning

confidence: 99%