Recognition of human activities using SVM multi-class classifier

Qian, Huimin; Mao, Yaobin; Xiang, Wenbo; Wang, Zhiquan

doi:10.1016/j.patrec.2009.09.019

Cited by 170 publications

(81 citation statements)

References 26 publications

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“…The main idea of SVM is to separate the classes with a hyperplane surface so as to maximise the margin between them. Following the structural risk minimisation (SrM) principle, SVM can effectively overcome over-fitting and under-fitting problems and provides a greater generalisation ability (Byun & Lee 2002;Guo et al 2006;Qian et al 2010). In this study, the radial basic function SVM (rBF-SVM) classifier (Guo et al 2006) was used to segment the bruise region on kiwifruit.…”

Section: Methodsmentioning

confidence: 99%

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

LüQiang¹,

Tang²,

Cai³

et al. 2011

Czech J. Food Sci.

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Lü Q., Tang M.-j., Cai j.-r., Zhao j.-w., Vittayapadung S. (2011): Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech j. Food Sci., 29: 595-602.It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.we proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIr (408-1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using rBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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Section: Methodsmentioning

confidence: 99%

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

LüQiang¹,

Tang²,

Cai³

et al. 2011

Czech J. Food Sci.

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“…It plans to acquire a global ideal through simulated annealing system without depending on model introduction to maintain a strategic distance from neighborhood minima. Qian et al [17] joined global features and nearby features to order and perceive human activities. The global feature depended on paired motion energy image (MEI) and its form coding of the MEI was utilized rather than MEI as a superior global feature in light of the fact that it defeats the impediment of MEI where hollows exist for parts of human blob are undetected.…”

Section: Action Recognition With Space-time Volumesmentioning

confidence: 99%

A Review on Machine Learning Algorithms on Human Action Recognition

Rai

2017

Asian J Pharm Clin Res

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Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistic-based methodologies, syntactic methodologies, and description-based methodologies for hierarchical recognition are examined in the paper.

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“…Support vector machine (SVM) is one of the most successful classifiers in discriminative models category. It has shown better performance than some old-style classifiers such as backpropagation neural networks, Naïve Bayes, and k-nearest neighbors (KNNs) in many classification problems [37]. SVM was first proposed in [38], and, originally, it was developed for binary classification but later on extended to the multiclass classification problem.…”

Section: Action Classification With Svm Multiclass Classifiermentioning

confidence: 99%

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016

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This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.

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Recognition of human activities using SVM multi-class classifier

Cited by 170 publications

References 26 publications

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

A Review on Machine Learning Algorithms on Human Action Recognition

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

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