ON/OFF switch control of smart home prototype using palm and fist hand gesture

Taban, Duaa A.; Al-Zuky, Ali A.; Al-Saleh, Anwar H.; Mohamad, Haidar J.; Daway, Hazim G.

doi:10.1063/5.0027540

Cited by 1 publication

(2 citation statements)

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“…AdaBoost algorithm [30] is used to combine weak classifiers from all levels to obtain strong classifier. The strong classifier is strong features used to detect ROI in images or real-time video [2]. The weak classifiers are obtained in a series of sequentially re-weighted updates of the training data [24].…”

Section: Boosted Cascade Classifiermentioning

confidence: 99%

“…Computer vision is a branch of research that enables machines to process, analyze, and interpret the content of digital images and videos in order to perceive visuals similarly to humans. It lets a computer recognize and detect objects in images, in other words [1,2]. Object detection is determining a possible location of region of interest (ROI) in input image or in real time video [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Target Detection in Video Images Using HOG-Based Cascade Classifier

Namah¹,

Mirza²,

Al-Zuky³

2022

RIA

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Detecting small objects using computer vision is a challenging task due to their small size in the image and therefore the lack of features when describing them. In this paper, a computer was trained to detect three small balls using 20 levels of the AdaBoost cascade classifier. The features of the balls in each level are described using the HOG feature descriptor. Three balls were recorded in practice at various distances (d = 2, 3, 4, ..., 10 m) from the camera and the targets (balls). The frames are then taken from the videos and resized using five magnification factors (RS = 1, 3, 5, 7, and 9) to make the balls seem as they should. According to the results, the detection rate of balls at all distances was 80% when using the magnification factor RS = 1, 90% when using the magnification factor RS = 3, 5, and 7, and 100% when using the magnification factor RS = 9. The suggested approach was also used in calculating the height and width of the detected balls. The overall results indicated that the height and width of the balls dwindle as the distance between the camera and the targets increases.

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Section: Boosted Cascade Classifiermentioning

confidence: 99%