Multi-layered Gesture Recognition with Kinect

Jiang, Feng; Zhang, Shengping; Wu, Shunchuan; Zhao, Debin

doi:10.1007/978-3-319-57021-1_13

Cited by 41 publications

(43 citation statements)

References 54 publications

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“…Long Short Term Memory (LSTM) networks have proven successful for this task [22]. With the release of ChaLearn Gesture Challenge data set [23], there have been a number of works in oneshot learning, in which a single training example is used per gesture class [24], [25], [26]. A third focus is on developing methods that work on well-established gesture sets, such as sign languages.…”

Section: Related Workmentioning

confidence: 99%

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Local frame match distance: A novel approach for exemplar gesture recognition

Ionescu

Popescu

Conly

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Gesture recognition using a training set of limited size for a large vocabulary of gestures is a challenging problem in computer vision. With few examples per gesture class, researchers often employ state-of-the-art exemplar-based methods such as Dynamic Time Warping (DTW). This paper makes two contributions in the area of exemplar-based gesture recognition. As an alternative to DTW, we first introduce the Local Frame Match Distance (LFMD), a novel approach for matching gestures inspired by a distance measure for strings, namely Local Rank Distance (LRD). While LRD efficiently approximates the non-alignment of character n-grams between two strings, we employ LFMD to efficiently measure the nonalignment of hand locations between two video sequences. Second of all, we transform LFMD into a kernel and use it in combination with Kernel Discriminant Analysis for sign language recognition with exemplars. The empirical results indicate that our method can generally yield better performance than a state-of-the-art DTW approach on the challenging task of American Sign Language recognition, while reducing the computational time by 30%.

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Section: Related Workmentioning

confidence: 99%

“…25 for i ∈ {1, ..., n − p + 1} do 26 for j ∈ {1, ..., n − p + 1} do where · 2 represents the L 2 -norm. For p-frames X i:i+p−1 and Y j:j+p−1 , we naively consider the cost given by diagonally aligning the individual frames:…”

Section: Local Frame Match Distancementioning

confidence: 99%

Local frame match distance: A novel approach for exemplar gesture recognition

Ionescu

Popescu

Conly

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Human gesture recognition is mainly performed on RGB-D (Red, Green, Blue and Depth) data [9], [10], [11], [12], [6], [13] or on skeleton data [14], [15], [16], [17], [18], [13], [19], [20], [21], where skeletal data can be extracted from RGB-D data. To recognize static gestures (i.e.…”

Section: Movementioning

confidence: 99%

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

Deboeverie

Roegiers

Allebosch

et al. 2016

2016 IEEE Conference on Computational Intelligence and Games (CIG)

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Abstract-In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.

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“…The main difficulty is that it is not easy to design a good appearance modeling method, which is not only good at distinguishing the target from its background but also being robust to the above-mentioned appearance changes. Finding a good appearance modeling is a challenging problem in many visual applications such as image classification [17][18][19] and video recognition [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Visual Tracking Based on Discriminative Compressed Features

Liu

Wang

2018

Advances in Multimedia

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Visual tracking is a challenging research topic in the field of computer vision with many potential applications. A large number of tracking methods have been proposed and achieved designed tracking performance. However, the current state-of-the-art tracking methods still can not meet the requirements of real-world applications. One of the main challenges is to design a good appearance model to describe the target's appearance. In this paper, we propose a novel visual tracking method, which uses compressed features to model target's appearances and then uses SVM to distinguish the target from its background. The compressed features were obtained by the zero-tree coding on multiscale wavelet coefficients extracted from an image, which have both the low dimensionality and discriminate ability and therefore ensure to achieve better tracking results. The experimental comparisons with several stateof-the-art methods demonstrate the superiority of the proposed method.

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Multi-layered Gesture Recognition with Kinect

Cited by 41 publications

References 54 publications

Local frame match distance: A novel approach for exemplar gesture recognition

Local frame match distance: A novel approach for exemplar gesture recognition

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

Visual Tracking Based on Discriminative Compressed Features

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