Hand Gesture Detection and Tracking Methods Based on Background Subtraction

Song, Wei; Zhang, Lu; Li, Jinhong; Li, Jie; Liao, Jinqiao; Cho, Kyungeun; Um, Kyhyun

doi:10.1007/978-3-642-55038-6_76

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Support Vector Machine (SVM) was used to train feature vectors and testing is done with previously learned SVM by comparing it with same gesture recognition at different lightning conditions but again problems arises when there is a complex background [7]. Song.W et al [6] has used background subtraction and frame difference techniques for recognizing hand gesture areas. The moving part is detected by subtracting the current image and eliminating the image background.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance comparison of segmentation algorithms for hand gesture recognition

Subramaniam

2018

IJET

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The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.

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Section: Literature Reviewmentioning

confidence: 99%

Performance comparison of segmentation algorithms for hand gesture recognition

Subramaniam

2018

IJET

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“…Human activity recognition (HAR) [3,15,31,32] is a branch of machine learning that trains a classification model using historical activity records to recognize unseen activities. HAR has wide application, ranging from abnormal behaviour detection in security surveillance [21] and posture monitoring in tele-rehabilitation [7], to hand gesture recognition in augmented-reality [30] and virtual-reality [18] systems, as well as intelligent home environments [14]. One type of HAR is based on motion sensor data from which the HAR system tries to infer activity patterns for prediction and classification.…”

Section: Introductionmentioning

confidence: 99%

GPU-based parallel Shadow Features generation at neural system for improving gait human activity recognition

Brito

Biuk-Aghai

Fong

2021

Multimed Tools Appl

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In this paper, we propose a new method for improving human activity recognition (HAR) datasets in order to increase their classification accuracy when trained with a certain classifier like a Neural Network. In this paper a novel training/testing process for building/testing a classification model for human activity recognition (HAR) is proposed. Traditionally, HAR is done by a classifier that learns what activities a person is doing by training with skeletal data obtained from a motion sensor such as Microsoft Kinect or accelerometer sensors. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. In addition to the spatial features that describe current positions in the skeletal data, new features called Shadow Features are used to improve the supervised learning efficiency and accuracy of Neural Network classifiers. Shadow Features are inferred from the dynamics of body movements, thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterizing activities in the classification process and thus significantly improving the accuracy. These Shadow Features are generated based on the existing features obtained from sensor datasets. In this paper we show that the accuracy of a neural network classifier can be significantly improved by the addition of Shadow Features and we also show that the generation of Shadow Features can be achieved with little time cost, on the fly, with the NVIDIA GPU technology and the CUDA programming model, this way we can improve the Neural Network accuracy at almost no time cost. GPUs are particularly suitable for generating Shadow Features, since they possess multiple cores which can be taken advantage of, in order to generate Shadow Features for multiple data columns in parallel, therefore reducing a lot of processing time, especially when dealing with huge datasets.

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“…HAR has wide application, ranging from abnormal behaviour detection in security surveillance [1] and posture monitoring in tele-rehabilitation [2], to hand gesture recognition in augmented-reality [3], and virtual-reality [4] systems, etc.…”

Section: Introductionmentioning

confidence: 99%

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Fong

Song

Cho

et al. 2017

Sensors

Self Cite

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In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

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Hand Gesture Detection and Tracking Methods Based on Background Subtraction

Cited by 5 publications

References 7 publications

Performance comparison of segmentation algorithms for hand gesture recognition

Performance comparison of segmentation algorithms for hand gesture recognition

GPU-based parallel Shadow Features generation at neural system for improving gait human activity recognition

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

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