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Proceedings ArticleDOI

Gesture recognition using DTW & piecewise DTW

TL;DR: Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified, and it is estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW.
Abstract: Classification of hand gesture is crucial for the development of hand gesture based system for human machine interaction. Gesture recognition system consists of hand gesture acquisition, segmentation, morphological filtering, contour extraction, and classification. This paper aims at classification of hand gesture as a similarity measure using Dynamic Time Warping and Piecewise Dynamic Time Warping. Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified. Additionally, it is also estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW.
Citations
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Proceedings ArticleDOI
01 Aug 2016
TL;DR: The proposed technique overcomes not only the limitations of a glove based approach but also most of the vision based approach concerning different illumination conditions, background complexity and distance from camera which is up to 2 meters.
Abstract: This paper presents an algorithm of Hand Gesture Recognition by using Dynamic Time Warping methodology. The system consists of three modules: real time detection of face region and two hand regions, tracking the hands trajectory both in terms of direction among consecutive frames as well as distance from the centre of the frame and gesture recognition based on analyzing variations in the hand locations along with the centre of the face. The proposed technique of ours overcomes not only the limitations of a glove based approach but also most of the vision based approach concerning different illumination conditions, background complexity and distance from camera which is up to 2 meters. Also by using Dynamic Time Warping Algorithm which finds the optimal alignment between the stored database features and query features, improvement in recognition accuracy is observed compared to conventional methods. Experimental results show that the accuracy is 90% in recognizing 24 gestures based on Indian Sign Language.

43 citations


Additional excerpts

  • ...The approach is to binarize the frame based on skin color so as to segment out the face region and the two regions of the hand....

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Proceedings ArticleDOI
01 Dec 2015
TL;DR: A realtime Kinect-based dynamic hand gesture recognition (HGR) system which contains hand tracking, data processing, model training and gesture classification is proposed and shows efficiency with an average recognition rate of 95.42% and real-time performance of the method.
Abstract: The use of hand gestures provides an attractive alternative to cumbersome interface devices for Human-Computer Interaction (HCI). However, in dynamic gesture recognition area, hand tracking under a complicated environment and gesture spotting namely detecting the start and end point are the two most challenging topics. In our work, a realtime Kinect-based dynamic hand gesture recognition (HGR) system which contains hand tracking, data processing, model training and gesture classification is proposed. In the first stage, two states of the performed hand including open and closed are utilized to achieve gesture spotting and 3D motion trajectories of gestures are captured by Kinect sensor. Further, motion orientation is extracted as the unique feature and Support Vector Machine (SVM) is used as the recognition algorithm in the proposed system. The results of experiments conducted in our database containing 10 Arabic numbers from 0 to 9 and the 26 characters of alphabet show efficiency with an average recognition rate of 95.42% and real-time performance of our method.

38 citations


Cites methods from "Gesture recognition using DTW & pie..."

  • ...While in 2014, Salvastore Ieng et al. presented a reliable and continuous gesture recognition method based on HMMs that supports a natural and flexible between the human and the robot [7]....

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Journal ArticleDOI
TL;DR: An HRI model of a robotic arm is proposed for robot arm manipulation using 3D SSD architecture for the location and identification of gesture and arm movement and DTW template matching algorithm is adopted to trace the dynamic gestures.
Abstract: Human–robot interaction (HRI) has become a research hotspot in computer vision and robotics due to its wide application in human–computer interaction (HCI) domain. Based on the explored algorithms of gesture recognition and limb movement recognition in somatosensory interaction, an HRI model of a robotic arm is proposed for robot arm manipulation. More specifically, 3D SSD architecture is used for the location and identification of gesture and arm movement. Then, DTW template matching algorithm is adopted to trace the dynamic gestures. The interactive scenarios and interactive modes are designed for experiment and implementation. Virtual interactive experimental results have demonstrated the usefulness of our method.

36 citations

Proceedings ArticleDOI
01 Oct 2015
TL;DR: A rapid recognition for dynamic hand gestures using leap motion is proposed, which contains the three-dimensional motion trajectory of the numbers and the alphabet which captured by utilizing a leap motion.
Abstract: Human Computer Interaction would be much more smooth with the implementation of rapid recognition, the aim of which is to recognize the hand gesture before it is completed In this paper, a rapid recognition for dynamic hand gestures using leap motion is proposed The database contains the three-dimensional motion trajectory of the numbers and the alphabet (36 gestures in total) which captured by utilizing a leap motion In order to enhance the effectiveness of rapid recognition, the SVM algorithm is utilized in the paper Experimental results show high recognition rate and accuracy of the proposed system

33 citations


Cites methods from "Gesture recognition using DTW & pie..."

  • ...Over years, various classification algorithms have been utilized to achieve a high recognition accuracy and rate in the gesture recognition area, such as HMM in [1],[10],[11],[12], SVM in [13] and DTW in [14]....

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Journal ArticleDOI
TL;DR: It is shown that the 3rd dimension, which essentially represents instantaneous pressure during writing, can improve the accuracy of the biometric systems and believe, Leap motion can be an alternative to the existing biometric setups.
Abstract: Signature recognition is identifying the signature’s owner, whereas verification is the process to find whether a signature is genuine or forged. Though, both are important in the field of forensic sciences, however, verification is more important to banks and credit card companies. In this paper, we have proposed a methodology to analyze 3D signatures captured using Leap motion sensor. We have extended existing 2D features into 3D from raw signatures and applied well-known classifiers for recognition as well as verification. We have shown that the 3rd dimension, which essentially represents instantaneous pressure during writing, can improve the accuracy of the biometric systems. We have created a large dataset containing more than 2000 signatures registered by 100 volunteers using the Leap motion interface. This has been made available online for the research community. Our analysis shows that, the proposed 3D extension is better than its original 2D version. Recognition and verification accuracy have increased by 6.8% and 9.5%, respectively using k-NN. Similarly, accuracy has increased by 9.9% (recognition) and 6.5% (verification) when HMM is used as the classifier. Similar results have been recorded on benchmark datasets. A comparison with 2D tablet-stylus interface has been carried out which also supports our claims. We believe, Leap motion can be an alternative to the existing biometric setups.

19 citations


Cites background from "Gesture recognition using DTW & pie..."

  • ...On the contrary, time-series data representing signatures can be directly matched using elastic distance measures such as Dynamic TimeWarping (DTW) [29]....

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  • ...We compute the maximum intra-class distance using DTW as mentioned in Algorithm 3 and make it as the centroid of the respective class....

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  • ...In DTW, we have used a cost function between two points S(x1, x2, ..xn) and T (y1, y2, ..yn) as given in (31), where x1, x2, ..xn and y1, y2, ..yn represent the high-level features of the respective points. cost (S, T ) = |S − T | = √√√√ i=n∑ i=1 (xi − yi)2 (31) We have used the a sequence of processes as depicted in Fig....

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  • ...Matching techniques such as DTW [29], Minimal Variance Matching (MVM) [23], Hidden Markov Model (HMM) [7], Sparse representation [39], Support vector machine (SVM) [1], Neural Networks (NN) [8], and Long Short TermMemory Neural Networks (LSTMNN) [4] have been used by various earlier reported research work....

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  • ...DTW [20] measures the elastic distance between the given sequences, hence it is a measure of similarities between two time series data that may vary in time or speed....

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References
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Journal ArticleDOI

37,017 citations


"Gesture recognition using DTW & pie..." refers methods in this paper

  • ...We selected white screen as background, Ostu thresholding algorithm [5] is applied for segmentation....

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01 Jan 1999
TL;DR: In this paper, a modification of DTW, called Piecewise Aggregate Approximation (PAA), is proposed to improve the robustness of time series distance calculation with no loss of accuracy.
Abstract: There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA). Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.

670 citations

Proceedings ArticleDOI
01 Aug 2000
TL;DR: This paper introduces a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA) which allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.
Abstract: There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA). Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.

667 citations


"Gesture recognition using DTW & pie..." refers methods in this paper

  • ...The algorithm, Piecewise Dynamic Time Warping (PDTW), takes advantage of the fact that we can efficiently approximate most time series by a piecewise aggregate approximation [6]....

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Journal ArticleDOI
TL;DR: The results suggest a new approach to dynamic time warping for isolated words in which both the reference and test patterns are linearly warped to a fixed length, and then a simplified dynamic time Warping algorithm is used to handle the nonlinear component of the time alignment.
Abstract: The technique of dynamic programming for the time registration of a reference and a test pattern has found widespread use in the area of isolated word recognition. Recently, a number of variations on the basic time warping algorithm have been proposed by Sakoe and Chiba, and Rabiner, Rosenberg, and Levinson. These algorithms all assume that the test input is the time pattern of a feature vector from an isolated word whose endpoints are known (at least approximately). The major differences in the methods are the global path constraints (i.e., the region of possible warping paths), the local continuity constraints on the path, and the distance weighting and normalization used to give the overall minimum distance. The purpose of this investigation is to study the effects of such variations on the performance of different dynamic time warping algorithms for a realistic speech database. The performance measures that were used include: speed of operation, memory requirements, and recognition accuracy. The results show that both axis orientation and relative length of the reference and the test patterns are important factors in recognition accuracy. Our results suggest a new approach to dynamic time warping for isolated words in which both the reference and test patterns are linearly warped to a fixed length, and then a simplified dynamic time warping algorithm is used to handle the nonlinear component of the time alignment. Results with this new algorithm show performance comparable to or better than that of all other dynamic time warping algorithms that were studied.

618 citations


"Gesture recognition using DTW & pie..." refers background in this paper

  • ...,wr where Wi indexes a position in the cost matrix[8]....

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Journal Article
TL;DR: This paper has compared several techniques for edge detection in image processing and found that the most efficient and scalable approach is the one that focuses on directly detecting the edges of an image.
Abstract: Edge detection is one of the most commonly used operations in image analysis, andthere are probably more algorithms in the literature for enhancing and detecting edgesthan any other single subject. The reason for this is that edges form the outline of anobject. An edge is the boundary between an object and the background, and indicatesthe boundary between overlapping objects. This means that if the edges in an image canbe identified accurately, all of the objects can be located and basic properties such asarea, perimeter, and shape can be measured. Since computer vision involves theidentification and classification of objects in an image, edge detections is an essential tool. In this paper, we have compared several techniques for edge detection in image processing.

603 citations