Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm

Gómez-Conde, Iván; Olivieri, David N.; Vila, Xosé A.; Orozco-Ochoa, S.

doi:10.4236/jsip.2011.24040

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Extraction of background Several methods can be used for detecting mobile elements in the scene like Gaussian Mixture Model [11], occupancy grid [12]. In this article we use a simple and fast method which consists in averaging the depth map for learning the background [11].…”

Section: Feature Extractionmentioning

confidence: 99%

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Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

Dubois

Charpillet

2013

Lecture Notes in Computer Science

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Abstract. Falls in the elderly is a major public health problem because of their frequency and their medical and social consequences. New smart assistive technologies and Health Telematics make it possible to provide elderly with more security and well being at home. A smart home can automatically monitor home activities for early warning in health changes or detecting dangerous situations. One of our objectives is to design an automatic system to detect fall at home, which in its final version will be made up of a network of RGB-D sensors. In this paper, we present a simple and robust method based on the identification and tracking of the center of mass of people evolving in an indoor environment. Using a simple Hidden Markov Model whose observations are the position of the center of mass, its velocity and the general shape of the body, we can surprisingly monitor the activity of a person with high accuracy and thus detect falls with very good accuracy without false positives. An experimental study, that is reported here, has been driven in our smart apartment lab. 26 subjects were asked to perform a predefined scenario in which they realized a set of eight postures. 2 hours of video (216 000 frames) were recorded for the evaluation, half of it being used for the training of the model. The system detected the falls without false positives. This result encourages us to use this system in real situation for a better study of its efficiency.

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Section: Feature Extractionmentioning

confidence: 99%

“…In this article we use a simple and fast method which consists in averaging the depth map for learning the background [11]. At each time we substract the background distances from the current distances to keep only mobile points.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

Dubois

Charpillet

2013

Lecture Notes in Computer Science

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“…To extract the background we use the "running average" method [6]. This technique allows to learn the background by averaging over time the distances for each point of the depth map.…”

Section: A Extraction Of Backgroundmentioning

confidence: 99%

“…Some use the size of the blob to know if the person is lying, sitting or standing [7]. Or some compute the number of pixels on vertical axis for each blob [6] or for different slices of blobs [3]. For the last, they consider a person has fallen if the sum of the third slices (from of ground) exceed a threshold.…”

Section: Introductionmentioning

confidence: 99%

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Human activities recognition with RGB-Depth camera using HMM

Dubois

Charpillet

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Fall detection remains today an open issue for improving elderly people security. It is all the more pertinent today when more and more elderly people stay longer and longer at home. In this paper, we propose a method to detect fall using a system made up of RGB-Depth cameras. The major benefit of our approach is its low cost and the fact that the system is easy to distribute and install. In few words, the method is based on the detection in real time of the center of mass of any mobile object or person accurately determining its position in the 3D space and its velocity. We demonstrate in this paper that this information is adequate and robust enough for labeling the activity of a person among 8 possible situations. An evaluation has been conducted within a real smart environment with 26 subjects which were performing any of the eight activities (sitting, walking, going up, squatting, lying on a couch, falling, bending and lying down). Seven out of these eight activities were correctly detected among which falling which was detected without false positives.

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RETRACTED: Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

Nandyal

Kattimani

2021

J. Phys.: Conf. Ser.

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Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature Extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.

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Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm

Cited by 8 publications

References 14 publications

Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model

Human activities recognition with RGB-Depth camera using HMM

RETRACTED: Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

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