Deformable Part Model Based Hand Detection against Complex Backgrounds

  • Chunyu Zou
  • Yue LiuEmail author
  • Jiabin Wang
  • Huaqi Si
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)


Hand detection is a challenging task in hand gesture recognition system and the detection results can be easily affected by changes in hand shapes, viewpoints, lightings or complex backgrounds. In order to detect and localize the human hands in static images against complex backgrounds, a hand detection method based on a mixture of multi-scale deformable part models is proposed in this paper, which is trained discriminatively using latent SVM and consists of three components each defined by a root filter and three part filters. The hands are detected in a feature pyramid in which the features are variants of HOG descriptors. The experimental results show that the proposed method is invariant to small deformations of hand gestures and the mixture model has a good performance on NUS hand gesture dataset - II.


Hand detection Deformable part model Latent SVM HOG features Complex backgrounds 



This research was supported by the National Natural Science Foundation of China under grant No. 61370134, the National High Technology Research and Development Program of China (863 Program) under grant No. 2013AA013904.


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of OptoelectronicsBeijing Institute of TechnologyBeijingChina

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