HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

Santos, Diego P. dos; Fernandes, Bruno; Bezerra, Byron Leite Dantas

doi:10.3390/s151128646

Cited by 28 publications

(14 citation statements)

References 43 publications

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“…Both techniques were combined with the Viterbi algorithm for arm motion gesture recognition [17]. Santos et al [18] presented a system for dynamic hand gesture recognition based on depth maps and a hybrid classifier that integrates dynamic time warping and Hidden Markov Models (HMMs). Milazzo et al [19] proposed a modular middleware to ease the development of gesture-based applications focused on Human-Computer Interaction.…”

Section: Related Workmentioning

confidence: 99%

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

et al. 2019

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Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of F-score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.

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Section: Related Workmentioning

confidence: 99%

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

et al. 2019

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“…In dynamic hand gesture, the information of gesture is contained in a sequence of images, so besides extracting gesture characteristics in each frame, the recognition approaches must perform the changes of gesture movement. After extracting spatial descriptors in each frame, temporal properties of hand gestures are created by common methods as using hand detecting and tracking [15,21,22] or using Dynamic Time Warping (DTW) [14,18], spending expensive cost to find the optimal alignment path or Temporal Pyramid [19,20], connecting with spatial location.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Shape and Motion Features for Dynamic Hand Gesture Recognition in Depth Video

Viet¹,

Phuc²,

Hoang³

et al. 2018

IJIGSP

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Human-Computer Interaction (HCI) is one of the most interesting and challenging research topics in computer vision community. Among different HCI methods, hand gesture is the natural way of humancomputer interaction and is focused on by many researchers. It allows the human to use their hand movements to interact with machine easily and conveniently. With the birth of depth sensors, many new techniques have been developed and gained a lot of achievements. In this work, we propose a set of features extracted from depth maps for dynamic hand gesture recognition. We extract HOG2 for shape and appearance of hand in gesture representation. Moreover, to capture the movement of the hands, we propose a new feature named HOF2, which is extracted based on optical flow algorithm. These spatial-temporal descriptors are easy to comprehend and implement but perform very well in multi-class classification. They also have a low computational cost, so it is suitable for real-time recognition systems. Furthermore, we applied Robust PCA to reduce feature's dimension to build robust and compact gesture descriptors. The robust results are evaluated by cross-validation scheme using a SVM classifier, which shows good outcome on challenging MSR Hand Gestures Dataset and VIVA Challenge Dataset with 95.51% and 55.95% in accuracy, respectively.

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“…In particular, the depths of interesting objects in images are very useful; for instance, the distances from obstacles in the road, frontal vehicles, and traffic lights must be known when an unmanned vehicle is running on the road. The depth information can also be used for pattern recognition [1,2].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

Yang

Kong

et al. 2017

Applied Sciences

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Abstract:Recovering depth information of objects from two-dimensional images is one of the very important and basic problems in the field of computer vision. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object's depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the most appropriate pair of straight line segments are chosen, and also reduce computation, convex hull theory and knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.

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HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

Cited by 28 publications

References 43 publications

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

Spatial-Temporal Shape and Motion Features for Dynamic Hand Gesture Recognition in Depth Video

An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

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