3-D hand motion tracking and gesture recognition using a data glove

Kim, Ji‐Hwan; Thang, Nguyen Duc; Kim, Tae‐Seong

doi:10.1109/isie.2009.5221998

Cited by 122 publications

(52 citation statements)

References 7 publications

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“…In [12] data gloves incorporating accelerometers are used to directly obtain hand position. To avoid the expense of such systems, authors such as Kadir et al [11] use vision techniques, coupled with gloves of specific colours, and ensure these colours are not present elsewhere in the scene.…”

Section: Related Work Hand Trackingmentioning

confidence: 99%

Image Analysis and Recognition

2012

Lecture Notes in Computer Science

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Abstract. Tracking hands and estimating their trajectories is useful in a number of tasks, including sign language recognition and human computer interaction. Hands are extremely difficult objects to track, their deformability, frequent self occlusions and motion blur cause appearance variations too great for most standard object trackers to deal with robustly. In this paper, the 3D motion field of a scene (known as the Scene Flow, in contrast to Optical Flow, which is it's projection onto the image plane) is estimated using a recently proposed algorithm, inspired by particle filtering. Unlike previous techniques, this scene flow algorithm does not introduce blurring across discontinuities, making it far more suitable for object segmentation and tracking. Additionally the algorithm operates several orders of magnitude faster than previous scene flow estimation systems, enabling the use of Scene Flow in real-time, and near real-time applications. A novel approach to trajectory estimation is then introduced, based on clustering the estimated scene flow field in both space and velocity dimensions. This allows estimation of object motions in the true 3D scene, rather than the traditional approach of estimating 2D image plane motions. By working in the scene space rather than the image plane, the constant velocity assumption, commonly used in the prediction stage of trackers, is far more valid, and the resulting motion estimate is richer, providing information on out of plane motions. To evaluate the performance of the system, 3D trajectories are estimated on a multi-view signlanguage dataset, and compared to a traditional high accuracy 2D system, with excellent results.

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Section: Related Work Hand Trackingmentioning

confidence: 99%

Image Analysis and Recognition

2012

Lecture Notes in Computer Science

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“…By employing PCA and Sequential Feature Selection techniques, dimension of the feature vector is reduced. For the experiment, different classifiers are applied and the experimental results confirm Also, due to the mass of attached wires, it is quite awkward for the users [6]. 2.…”

Section: Introductionmentioning

confidence: 96%

Intelligent visual mouse system based on hand pose trajectory recognition in video sequences

Maleki

Ebrahimnezhad

2014

Multimedia Systems

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“…However, with this method it is difficult to track the features of the user's movement because of the influence of light, surrounding objects, distance limitations, and other variables. (6) Cheap products such as Kinect v2 from Microsoft were recently launched. This is very advantageous in terms of cost.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition with Inertial Sensors and a Magnetometer

2016

Sensors and Materials

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Due to their low cost and compact size, micro-electromechanical system-based inertial sensors have been utilized to detect human motion. This study presents a hand gesture recognition method with a three-axis accelerometer, gyroscope, and magnetometer attached to a wrist. First, sensor signals were applied to calculate the sensor's orientation with a Kalman filter. These data were used to convert the sensor-frame acceleration signal to a global-frame acceleration signal (orientation calculation). Acceleration of motion was calculated by subtracting gravity from the global acceleration signal. The start and end of hand motion was detected by motion acceleration (motion segmentation). Segmented hand motion was recognized by gesture (gesture recognition). Six gestures (up, down, left, right, click, and double-click) were selected for implementation of the system, and the performance was evaluated.

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3-D hand motion tracking and gesture recognition using a data glove

Cited by 122 publications

References 7 publications

Image Analysis and Recognition

Image Analysis and Recognition

Intelligent visual mouse system based on hand pose trajectory recognition in video sequences

Hand Gesture Recognition with Inertial Sensors and a Magnetometer

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