Robust gesture recognition using feature pre-processing and weighted dynamic time warping

Arıcı, Tarık; Celebi, Sait; Aydın, Ali Selman; Temiz, Talha Tarik

doi:10.1007/s11042-013-1591-9

Cited by 60 publications

(38 citation statements)

References 30 publications

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“…The success percentages of the studies performed with the literature studies that using same dataset are given in Table 3. Weighted DTW %97.13 [22] WDTW With Keyframes %100…”

Section: A Resultsmentioning

confidence: 99%

Human Gesture Recognition using Keyframes on Local Joint Motion Trajectories

Durgut¹,

Fındık²

2017

ijacsa

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Abstract-Human Action Recognition (HAR) systems are systems that recognize and classify the actions that users perform against the sensor or camera. In most HAR systems, an input test data is compared with the reference data in the database using various methods. Classification process is performed according to the result obtained. The size of the test or reference data directly affects the operation speed of the system. Reduced data size allows a significant performance increase in system operation speed. In this study, action recognition method is proposed by using skeletal joint information obtained by Microsoft Kinect sensor. Splitting keyframes are obtained from the skeletal joint information. The keyframes are observed as a distinguishing feature. Therefore, these keyframes are used for the classification process. Keeping the keyframes instead of keeping the position or angle information of action in the reference database can benefit from memory and working time. The weight value of each keyframes is calculated in the method. The problem of temporal differences that occur when comparing test and reference action is solved by Dynamic Time Warping (DTW). The k-nearest neighbor's algorithm is used for classification according to the obtained results from DTW. The sample has been tested in a data set so that the success of the method can be tested. As a result, 100% correct classification was achieved. It is also suitable for working at real time systems. Breakpoints can also be used to provide feedback to the user as a result of the classification process. The magnitude and direction of the keyframes, the change in the trajectory of joint, the position and the time of its existence also give information about the time errors.

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“…The success percentages of the studies performed with the literature studies that using same dataset are given in Table 3. Weighted DTW %97.13 [22] WDTW With Keyframes %100…”

Section: A Resultsmentioning

confidence: 99%

Human Gesture Recognition using Keyframes on Local Joint Motion Trajectories

Durgut¹,

Fındık²

2017

ijacsa

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“…In case of motion representation with the constant length, in which each action has same number of representation features, no matter how many samples original recording has, authors reports using popular classifiers like for example support vector machines [44], neural networks [32] or even nearest-neighbor [63,70]. When the continues data stream of MoCap signal classified (like it is in our case) the HMM [26,45] and DTW [3,63] are most common approaches. In this paper we have chosen DTW because its application is more straightforward then HMM classifier.…”

Section: Application For Human Actions Recognitionmentioning

confidence: 99%

“…In cases when hardware returns directly spatial coordinates of the body joints authors prefer to use feature that are derived from those coordinates. Among those features are various configurations of angle-based [6,10,46] and coordinate-based features [2,12]. Basing on our previous researches [26] the angle based-features gives better recognition results than coordinate -based.…”

Section: Application For Human Actions Recognitionmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates' preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions Multimed Tools Appl (2018) 77:30353-30380 recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave -one -out cross -validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.

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“…The usage of this method has been reported in multiple papers. In [13] DTW computes a dissimilarity of movement measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. The method [14] is based on histograms of action poses, extracted from MoCap data that are computed according to Hausdorff distance.…”

Section: Approaches To Actions Recognitionmentioning

confidence: 99%

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

2015

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Abstract:In this paper we propose a novel algorithm that enables online actions segmentation and classification. The algorithm enables segmentation from an incoming motion capture (MoCap) data stream, sport (or karate) movement sequences that are later processed by classification algorithm. The segmentation is based on Gesture Description Language classifier that is trained with an unsupervised learning algorithm. The classification is performed by continuous density forward-only hidden Markov models (HMM) classifier. Our methodology was evaluated on a unique dataset consisting of MoCap recordings of six Oyama karate martial artists including multiple champion of Kumite Knockdown Oyama karate. The dataset consists of 10 classes of actions and included dynamic actions of stands, kicks and blocking techniques. Total number of samples was 1236. We have examined several HMM classifiers with various number of hidden states and also Gaussian mixture model (GMM) classifier to empirically find the best setup of the proposed method in our dataset. We have used leave-one-out cross validation. The recognition rate of our methodology differs between karate techniques and is in the range of 81% ± 15% even to 100%. Our method is not limited for this class of actions but can be easily adapted to any other MoCap-based actions. The description of our approach and its evaluation are the main contributions of this paper. The results presented in this paper are effects of pioneering research on online karate action classification.

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Robust gesture recognition using feature pre-processing and weighted dynamic time warping

Cited by 60 publications

References 30 publications

Human Gesture Recognition using Keyframes on Local Joint Motion Trajectories

Human Gesture Recognition using Keyframes on Local Joint Motion Trajectories

Averaging of motion capture recordings for movements’ templates generation

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

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