Random Forest Based Gesture Segmentation from Depth Image
Gesture image segmentation is a challenge task due to the high degree of freedom of human gestures, large differences in shape and high flexibility, traditional pattern recognition and image processing methods are not effective in gesture detection. The traditional image segmentation based on the detection of skin color and the image of the depth image are limited by the effects of ambient light, skin color difference and image depth variation, resulting in unsatisfactory results. Therefore, we propose a hand gesture depth image segmentation method based on random forest. The method learns the gesture image feature representation of the depth image by supervising learning. Experiments show that the proposed method segments the gesture s’ pixels from the backgrounds area of the depth image. The proposed method potential has widely usages in gesture tracking, gesture recognition and human computer interaction.
KeywordsRandom forest Gesture segmentation Depth image
This research work is supported by the grant of Guangxi science and technology development project (No: AC16380124), the grant of Guangxi Science Foundation (No: 2017GXNSFAA198226), the grant of Guangxi Key Laboratory of Trusted Software of Guilin University of Electronic Technology (No: KX201513), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), and the grant of Innovation Project of GUET Graduate Education (2018YJCX43).
- 2.Bretzner, L., Laptev, I., Lindeberg, T.: Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002, (pp. 423–428). IEEE (2002)Google Scholar
- 3.Chen, Q., Georganas, N.D., Petriu, E.M.: Real-time vision-based hand gesture recognition using haar-like features. In: Proceedings of the 2007 Instrumentation and Measurement Technology Conference. IMTC 2007, pp. 1–6. IEEE (2007)Google Scholar
- 4.Bilal, S., Akmeliawati, R., El Salami, M.J., Shafie, A.A.: A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking. In: 2010 International Conference on Mechatronics and Automation (ICMA), pp. 934–939. IEEE (2010)Google Scholar
- 5.Jo, Y.G., Lee, J.Y., Kang, H.: Segmentation tracking and recognition based on foreground-background absolute features, simplified SIFT, and particle filters. In: IEEE Congress on Evolutionary Computation, 2006. CEC 2006, pp. 1279–1284. IEEE (2006)Google Scholar
- 6.Hong, H., Zhu, X.: A human hand-image detection based on skin-color and circular degree. In: 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, 2009. SNPD 2009, pp. 373–376. IEEE (2009)Google Scholar
- 9.Velloso, M.L.F., Carneiro, T.A., de Souza, F.J.: Unsupervised change detection using fuzzy entropy principle. In: Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS 2004, vol. 4, pp. 2550–2553. IEEE (2004)Google Scholar
- 10.Malassiotis, S., Aifanti, N., Strintzis, M.G.: A gesture recognition system using 3D data. In: Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission 2002, pp. 190–193. IEEE (2002)Google Scholar
- 12.Tang, M.: Recognizing hand gestures with Microsoft’s Kinect. Department of Electrical Engineering of Stanford University, Palo Alto (2011)Google Scholar