Hand Detection

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# Hand Detection - PowerPoint PPT Presentation

Hand Detection. Zhong Zhang. Skin and motion detector. A skin color likelihood distribution and a non-skin color distribution, denoted as and respectively are proposed. The probability of a pixel, whose color vector is [ r,g,b ], being skin is defined using Bayes rule:

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## PowerPoint Slideshow about 'Hand Detection' - reese-walter

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Presentation Transcript

### Hand Detection

Zhong Zhang

Skin and motion detector
• A skin color likelihood distribution and a non-skin color distribution, denoted as and respectively are proposed.
• The probability of a pixel, whose color vector is [r,g,b], being skin is defined using Bayes rule:
• Motion detector is based on frame differencing which works as follows:
• Let denote the intensity value at pixel , at the i-th frame.
• By comparing with and , we compute a motion indicator value .
Skin and Motion Detector

Top 1 candidate

Skin indicator

Motion indicator

Skin and motion indicator

Result

Mp: hand detection using multiple proposals. Sm: skin and motion detector. The detection is considered as correct if the distance between the center of the detection box and annotation box is less than half of face box width. The box size is [35 35].

Result

Mp: hand detection using multiple proposals. Sm: skin and motion detector. The detection is considered as correct if the overlap score between detection and annotation is larger than 0.5

Result

The detection is considered as correct if the overlap score between detection and annotation is larger than a threshold. In the this table, this threshold can be 0.3, 0.4 and 0.5