Smart Electronic Switching (ON/OFF) System Based on Real-time Detection of Hand Location in the Video Frames

Taban, Duaa A.; Al-Zuky, Ali A.; Kafi, Shaymaa H.; Al-Saleh, Anwar H.; Mohamad, Haidar J.

doi:10.1088/1742-6596/1963/1/012002

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Taban et.al. [19] depending on the computer vision, specifically the use of the Viola-Jones algorithm to detect and classify hand gestures, and through these gestures, the lights are turned on and off. The lighting response time to the gesture commands was 0.43 seconds, and the dataset consisted of eight gestures containing 12000 images.…”

Section: Related Workmentioning

confidence: 99%

Tracked Robot Control with Hand Gesture Based on MediaPipe

Wameed,

M. ALKAMACHI,

Erçelebi

2023

alkej

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Hand gestures are currently considered one of the most accurate ways to communicate in many applications, such as sign language, controlling robots, the virtual world, smart homes, and the field of video games. Several techniques are used to detect and classify hand gestures, for instance using gloves that contain several sensors or depending on computer vision. In this work, computer vision is utilized instead of using gloves to control the robot's movement. That is because gloves need complicated electrical connections that limit user mobility, sensors may be costly to replace, and gloves can spread skin illnesses between users. Based on computer vision, the MediaPipe (MP) method is used. This method is a modern method that is discovered by Google. This method is described by detecting and classifying hand gestures by identifying 21 three-dimensional points on the hand, and by comparing the dimensions of those points. This is how the hand gestures are classified. After detecting and classifying the hand gestures, the system controls the tracked robot through hand gestures in real time, as each hand gesture has a specific movement that the tracked robot performs. In this work, some important paragraphs concluded that the MP method is more accurate and faster in response than the Deep Learning (DL) method, specifically the Convolution Neural Network (CNN). The experimental results shows the accuracy of this method in real time through the effect of environmental elements decreases in some cases when environmental factors change. Environmental elements are such light intensity, distance, and tilt angle (between the hand gesture and camera).The reason for this is that in some cases, the fingers are closed together, and some fingers are not fully closed or opened and the accuracy of the camera used is not good with the changing environmental factors. This leads to the inability of the algorithm used to classify hand gestures correctly (the classification accuracy decrease), and thus response time of the tracked robot's movement increases. That does not present possibility for the system to determine whether the finger is closed or opened.

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Section: Related Workmentioning

confidence: 99%

Tracked Robot Control with Hand Gesture Based on MediaPipe

Wameed,

M. ALKAMACHI,

Erçelebi

2023

alkej

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“…Hand gesture detection is achieved using vision techniques, which are more natural and do not require any gadgets or sensors that obstruct hand movement [8]. Different types of cameras were utilized as sensors in vision methods to detect hand motions.…”

Section: Introductionmentioning

confidence: 99%

“…The Aggregated Channel Features (ACF) detector employs a boosted tree to aggregate 10 channels, and the channels used are the same [8]: (3 channels) for LUV color space, (6 channels) for histogram of oriented gradient (HoG), and a normalized gradient (1 channel). The ACF object detector recognizes specific objects in images based on the training images and the object ground truth locations used with the trained object detector function.…”

Section: Introductionmentioning

confidence: 99%

Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

Mirza¹,

Taban²,

Karam³

et al. 2022

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Static hand gesture recognition is critical in the development of a system for human-computer interaction. Many human-computer interactions, such as human-robot interaction, game control, control of smart home devices, and others, use hand gestures as a fundamental and natural language of the body. The direction of rotation of static hand gestures is the subject of this research, and the focus is on six degrees of rotation (0°, 45°, 90°, 180°, 270°, and 315°). This work presents an ideal approach that can recognize the angle of hand gestures based on the Aggregate Channel Features (ACF) detector. This approach consists of three main stages: preprocessing (image labelling), computer training, and hand angle detection based on the ACF detector. The training process consists of 25 stages. The static hand gesture dataset contained 569 images (361 for training and 208 for testing). The average time cost to detect all hand gesture angles was 0.9445 seconds, and all hand angles were recognized with 100% accuracy. This is a strong indication that supports our approach.

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Smart Electronic Switching (ON/OFF) System Based on Real-time Detection of Hand Location in the Video Frames

Cited by 2 publications

References 4 publications

Tracked Robot Control with Hand Gesture Based on MediaPipe

Tracked Robot Control with Hand Gesture Based on MediaPipe

Static Hand Gesture Angle Recognition via Aggregated Channel Features (ACF) Detector

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