Phase Feature-based Activity Level Estimation for Assisted Living

Pal, Sandipan; Abhayaratne, Charith

doi:10.1049/ic.2016.0059

Cited by 2 publications

(3 citation statements)

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“…Firstly, it can use a self-attention mechanism to find the relationship between different regions; secondly, a key frame sampling mechanism HAR based on the frequency domain are well-known due to its robustness against blurring, geometric changes and intensity changes [40]. In addition, it is computationally efficient for implementation [41,42]. Tran et al [43] have used the frequency domain features to minimize the variability of effect and achieved 82.7% accuracy on the KTH dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Vision-based Human Activity Recognition Using Local Phase Quantization

Pandey,

Mishra,

Khare

2024

JAIT

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Human activity recognition (HAR) has been the most active and interesting area of research in recent years due to its wide range of applications in the field, such as healthcare, security and surveillance, robotics, gaming, entertainment, etc. However, recognizing vision-based human activity is still a challenging as input sequences may have cluttered background, illumination conditions, occlusions, degradation of video quality, blurring, etc. In the literature, several state-of-the-art methods have been trained and tested on different datasets but have yet to perform adequately to a certain extent. Moreover, extracting potential features and combining appropriate methods is one of the most challenging tasks in realistic video. This paper proposes an efficient frequency-based blur-invariance local phase quantization feature extractor and multiclass SVM classifier that overcomes these challenges. The feature is invariant towards camera motion, misfocused optics, movements in the scene, and environmental conditions. The proposed feature vector is then fed to the classifier to recognize human activities. The experiment has conducted on two publicly available datasets, UCF101 and HMDB51, and has achieved 99.79% and 98.67% accuracies, respectively. The approach has also outperformed the existing state-of-the-art approaches in terms of computational cost without compromising the accuracy of HAR.

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Section: Literature Reviewmentioning

confidence: 99%

Vision-based Human Activity Recognition Using Local Phase Quantization

Pandey,

Mishra,

Khare

2024

JAIT

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“…In recent years, frequency domain techniques became popular due to their robustness to intensity and geometry changes, ability to measure large displacement and the fact that they are computationally more efficient for implementation [13,49]. Imtiaz et al developed an action recognition scheme based on extracting features from spectral domain.…”

Section: Image and Vision Computingmentioning

confidence: 99%

“…Human action recognition (HAR) is an important topic in computer vision due to its applications in assisted living, smart surveillance systems, human-computer interaction, computer gaming and affective computing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Depending on the target application, an action recognition system can be used to either recognize full body behavior [1], or to recognize partial body like gesture recognition [17] and facial recognition [18].…”

Section: Introductionmentioning

confidence: 99%

Handcrafted Localized Phase Features for Human Action Recognition

Hejazi

Abhayaratne

2022

SSRN Journal

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Phase Feature-based Activity Level Estimation for Assisted Living

Cited by 2 publications

References 0 publications

Vision-based Human Activity Recognition Using Local Phase Quantization

Vision-based Human Activity Recognition Using Local Phase Quantization

Handcrafted Localized Phase Features for Human Action Recognition

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