Re-sampling for Chinese Sign Language Recognition

Wang, Chunli; Chen, Xilin; Gao, Wen

doi:10.1007/11678816_7

Cited by 1 publication

(2 citation statements)

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“…However, the biggest problem with most work on sign language recognition is that only the recognition of isolated signs is considered [15,16]. In contrast to these works, here we work on the recognition of continuous sign language.…”

Section: Introductionmentioning

confidence: 99%

“…Since the computational demands for the proposed procedure are very high, we additionally propose some approximations which greatly ease the computational burden. Second, in sign language recognition, the lack of available data [6,16] is addressed by using virtual training samples from the existing data by cropping several regionsof-interest for each frame of a video sequence.…”

Section: Introductionmentioning

confidence: 99%

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Efficient approximations to model-based joint tracking and recognition of continuous sign language

Dreuw

Förster

Deselaers

et al. 2008

2008 8th IEEE International Conference on Automatic Face &Amp; Gesture Recognition

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We propose several tracking adaptation approaches to recover from early tracking errors in sign language recognition by optimizing the obtained tracking paths w.r.t. to the hypothesized word sequences of an automatic sign language recognition system. Hand or head tracking is usually only optimized according to a tracking criterion. As a consequence, methods which depend on accurate detection and tracking of body parts lead to recognition errors in gesture and sign language processing. We analyze an integrated tracking and recognition approach addressing these problems and propose approximation approaches over multiple hand hypotheses to ease the time complexity of the integrated approach. Most state-of-the-art systems consider tracking as a preprocessing feature extraction part. Experiments on a publicly available benchmark database show that the proposed methods strongly improve the recognition accuracy of the system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%