Posture recognition invariant to background, cloth textures, body size, and camera distance using morphological geometry

Silapasuphakornwong, Piyarat; Phimoltares, Suphakant; Lursinsap, Chidchanok; Hansuebsai, Aran

doi:10.1109/icmlc.2010.5580930

Cited by 15 publications

(9 citation statements)

References 8 publications

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“…Ref. [5] uses morphological geometry to identify postures whereas [7] deals with posture recognition using decision trees, nevertheless both of them deal with basic postures such as standing, bending, sitting and lying. Ref.…”

Section: Resultsmentioning

confidence: 99%

“…Noisy background, improper texture of the dress and an inappropriate distance of the dancer from the camera leads to erroneous results. The authors in [5] propose an algorithm using histograms to successfully deal with the above mentioned problems. In [6], the authors detect basic human postures with the help of active contours and neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy image matching for posture recognition in ballet dance

Saha¹,

Banerjee²,

Basu³

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This work aims at designing a fuzzy matching algorithm that would automatically recognize an unknown ballet posture from seventeen fundamental ballet dance primitives. A novel and simple 7-stage system is proposed to achieve the desired objective. Minimized skeletons of the dance postures are generated after performing skin color segmentation on them. Straight line approximation on the minimized skeletons with the help of chain code and sampling generate their equivalent stick figure diagrams. Significant straight lines from the stick figure diagrams are considered, their fuzzy membership with respect to the 4 quadrants are evaluated. Finally with the help of the evaluated data, a fuzzy T-norm operator determines the proximity of a generated dance posture with the seventeen fundamental dance primitives.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy image matching for posture recognition in ballet dance

Saha¹,

Banerjee²,

Basu³

et al. 2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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show abstract

“…Recognizing human behaviors using computer vision techniques is actively researched in various fields. A simple method detects a fall by analyzing the aspect ratio of the bounding box of a moving object [1][2][3][4][5]. Another design [6] evaluates whether or not a fall occurs by using motion history image and the ratio and angle of ellipse.…”

Section: Related Workmentioning

confidence: 99%

“…The boundary of foreground objects is described using the bounding box. In most works [2,5,6,9,14,15], the discrimination between normal state and fall state uses the ratio of human body. By using the variance in the ratio of the human body, the proposed system detects whether or not the posture has changed.…”

Section: Feature Extractionmentioning

confidence: 99%

“…In (4), the parameters ( ), ℎ( ) denote width and height of the bounding box at current time , respectively, and ( ) represents the ratio of width and height. In (5), the value ( ) refers to the mean value of the aspect ratio at current time and value ( − 1) denotes the mean value of the aspect ratio at previous time ( − 1). Value is the updated parameter.…”

Section: Feature Extractionmentioning

confidence: 99%

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An Indoor Video Surveillance System with Intelligent Fall Detection Capability

Chen

Liu

2013

Mathematical Problems in Engineering

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This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN) algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.

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Radon Transform Based Automatic Posture Recognition in Ballet Dance

Konar

Saha

2017

Gesture Recognition

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Posture recognition invariant to background, cloth textures, body size, and camera distance using morphological geometry

Cited by 15 publications

References 8 publications

Fuzzy image matching for posture recognition in ballet dance

Fuzzy image matching for posture recognition in ballet dance

An Indoor Video Surveillance System with Intelligent Fall Detection Capability

Radon Transform Based Automatic Posture Recognition in Ballet Dance

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