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

Maleki, Behnam; Ebrahimnezhad, Hossein

doi:10.1007/s00530-014-0420-y

Cited by 8 publications

(3 citation statements)

References 32 publications

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“…In this work, three basic gestures for hand and object grasping is defined, including pad opposition, palm opposition, and side opposition. Maleki and Ebrahimnezhad (2015) performed tasks involving grasp, scroll down, scroll up, next slide, previews slide, right click, left click, clockwise rotation, counterclockwise rotation, zoom in, and zoom out. Moving, orientation, scaling, and navigation tasks are done in a virtual environment in work by Hernoux and Christmann (2015).…”

Section: Interaction Between Hand and Objectsmentioning

confidence: 99%

Object manipulation and deformation using hand gestures

Mahdikhanlou

Ebrahimnezhad

2021

J Ambient Intell Human Comput

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Compared to the mouse, as a two-dimensional and precise interface device, hand gestures provide more degrees of freedom for users to interact with computers by employing intelligent computing methods. Leap Motion Controller is gaining more popularity due to its ability to detect and track hand joints in three dimensions. However, in some cases, the Leap Motion Controller measurements are not correct enough. We show that the occlusion, palm angle, and the limited field of view are the main downsides of the Leap Motion Controller. In this paper, a framework is proposed to manipulate and deform a three-dimensional object by hand gestures. We select only a few gestures so that the system instructions can be easily memorized. The gestures are not defined very strictly, so users can do them properly without getting tired. We propose that calculating a reliable space from a Leap Motion Controller can significantly reduce these problems. To deform objects, the Free Form Deformation technique is used, which allows for more local deformation. The selected gestures and determined space for interaction make the deformation framework achieve a balance between the accuracy, user-factors, required tasks for deformation, and limitation of the hand tracking device. The proposed method, compared to related studies, offers more creative methods for deforming objects and more natural movements to interact with the system. According to the conducted user study, a significant difference is observed between hand gesture interaction and mouse in terms of speed and number of attempts.

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Section: Interaction Between Hand and Objectsmentioning

confidence: 99%

Object manipulation and deformation using hand gestures

Mahdikhanlou

Ebrahimnezhad

2021

J Ambient Intell Human Comput

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“…Ali et al [10] proposed a visual tracking system using the Kalman filter. Maleki and Ebrahim-zadeh [11] developed a visual mouse system using hand motion recognition in consecutive images and finally, Khandar et al [12] proposed a mouse control system using vision-based head movement tracking.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Oskoei

2017

Robotics

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This paper proposes an adaptive Kalman filter (AKF) to improve the performance of a vision-based human machine interface (HMI) applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.

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“…The hand shape features extracted from a hand gesture image by regenerating a hand boundary image. Maleki B. and Ebrahimnezhad H. [13] proposed a visual mouse system based on dynamic hand gesture recognition. A white glove with different color fingertips is utilized to define 11 dynamic gestures based on mouse functions.…”

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confidence: 99%

Control of Robot Directions Based on Online Hand Gestures

Tawfeeq¹,

Abbass²

2018

IJEEE

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The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.

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

Cited by 8 publications

References 32 publications

Object manipulation and deformation using hand gestures

Object manipulation and deformation using hand gestures

Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

Control of Robot Directions Based on Online Hand Gestures

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