Extraction of Discriminative Patterns from Skeleton Sequences for Accurate Action Recognition

Thanh, Tran Thang; Fan, Chen; Kotani, Kazuhiko; Le, Bac

doi:10.3233/fi-2014-991

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Thanh et al [42] proposed the method of transforming joints information into three-dimensional skeleton histogram, and Sempena et al [43] proposed the method of the quaternion characteristics extraction, whose purpose was to transform the three-dimensional coordinate information into the characteristics quantity for recognition. However, they did not consider the internal structure of human body.…”

Section: Skeleton Characteristics Extractionmentioning

confidence: 99%

Posture Recognition Technology Based on Kinect

Chu

Xin

2020

IEICE Trans. Inf. & Syst.

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Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and threedimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

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Section: Skeleton Characteristics Extractionmentioning

confidence: 99%

Posture Recognition Technology Based on Kinect

Chu

Xin

2020

IEICE Trans. Inf. & Syst.

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“…To learn discriminative pose, a modified k-Nearest Neighbours (kNN) classifier is used that considers both the temporal location of a particular frame within the action sequence as well as the discrimination power of its moving pose descriptor compared to other frames in the training set. Thanh et al [14] extracted key frames which are the central frames in the short temporal segments of videos and labelled each key frame as a pattern for a unit action. An improved Term Frequency-Inverse Document Frequency (TF-IDF) method is used to learn the discriminative patterns and learned patterns is defined as local features for action recognition.…”

Section: Related Workmentioning

confidence: 99%

Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation

Wang

Ogunbona

et al. 2014

2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.

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“…The former uses 3D coordinates of the joints to represent the model of full human body. The method proposed by Thanh and Chen [ 6 ] falls into this type, which extracted the discriminative patterns as local features to classify skeleton sequences in human action recognition and the key frames were constructed based on skeleton histogram. Many other works tried to study the spatial-temporal descriptions from Kinect skeleton data, e.g., the angular representation [ 7 ] and skeletal shape trajectories [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

Zhang

Liu

et al. 2021

Sensors

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Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.

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Extraction of Discriminative Patterns from Skeleton Sequences for Accurate Action Recognition

Cited by 10 publications

References 14 publications

Posture Recognition Technology Based on Kinect

Posture Recognition Technology Based on Kinect

Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation

FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

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