RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization

Yogameena, B.; Veeralakshmi, S.; Komagal, E.; Raju, S. M. Taslim Uddin; Abhaikumar, V.

doi:10.1155/2009/164019

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…This enabled a fast and more flexible yet accurate simulation of head poses to be achieved with minimum memory requirements (Choi and Kim, 2008;Dornaika and Davoine, 2008;Jenab and Rashidi, 2009;Yogameena et al, 2010;Harkouss et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

Iskandarani¹

2011

Journal of Computer Science

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A reliable algorithm for head movements inside a vehicle is designed. The proposed algorithm allowed the adjustment of basic functions such as indicators, mirrors and reverse lights based on the driver final head position. The algorithm system mapped a predefined coordinates for driver's head that resulted in a computable geometry via a sensory system which is fed to the vehicle actuating system. Problem statement: Head position recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. In the past few years face detection and person identification became important issues due to security concerns, leading to head gesture algorithm development and implementation. Approach: This study introduces a new approach that combines Fixed Center Interpolation Net Algorithm (FCIN) with Wight Elimination Algorithm (WEA). This enhances the ability to classify and predict head positions and poses and gives better representation capabilities for the overall system algorithm. Such algorithm is able to handle pattern recognition problems using Radial Basis Function (RBF) models. The system algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. Results: A reliable, fast and robust approach for driver head position recognition is achieved and presented. Conclusion: A simple hybrid algorithm for driver's head movements is designed and tested. The obtained results proved the algorithm applicability and ability to predict and act upon head gestures.

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Section: Discussionmentioning

confidence: 99%

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

Iskandarani¹

2011

Journal of Computer Science

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“…It performed segmentation by continuous monitoring of the brightness of pixels over time. This technique has been used in many applications related to pattern recognition (Yogameena et al, 2009;Yogameena et al, 2010). Carlos et al (2008) introduced a new and efficient strategy for segmentation which reduced the computational cost of stauffer's technique (Stauffer and Grimson, 1999) as it used dynamic number of Gaussian per pixel.…”

Section: Segmentation Based On Mixture Of Gaussiansmentioning

confidence: 99%

A comparative study on mixture of Gaussians for object segmentation

Khatoon

2017

Int. j. adv. appl. sci

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Segmentation is the fundamental step in most of digital image processing and computer vision based applications for feature extraction. The purpose of segmentation is to partition an image into foreground and background. Numerous segmentation algorithms have been proposed for the last four decades ranging from degraded images; high and low contrast images, indoor video, outdoor videos, videos with static background and dynamic backgrounds. This paper presents evaluation and comparison of segmentation techniques used for real-time moving objects through static and adaptive number of Gaussians. The techniques are tested for both indoor and outdoor scenes. The comparison is presented on the basis of qualitative results and computational complexities.

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“…Support vector machines project the data into a higher dimensional space and maximize the margins between classes or minimize the error margin for regression (Shahrabi et al, 2009;Yogameena et al, 2010). A complexity parameter permits the adjustment of the number of error versus the model complexity and different kernels, such as the Radial Basis Function RBF) kernel, can be used to permit non-linear mapping into the higher dimensional space.…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

Sakthivel¹,

Rajaram²

2011

Journal of Computer Science

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Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.

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RVM-Based Human Action Classification in Crowd through Projection and Star Skeletonization

Cited by 12 publications

References 11 publications

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

A comparative study on mixture of Gaussians for object segmentation

Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

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