The DESO filter based on the template matching alogrithm

A novel approved algorithm based on template matching is proposed. This algorithm is used to predict the movement of the target and track the target. By utilizing the DESO(Differential Extended State Observer) filter, it is possible to obtain the estimate position of the target in the current image. The template matching algorithm is used to search target around the estimation position. The simulation results of the algorithm are presented. The proposed algorithm demonstrates better performance characteristics than traditional template matching algorithm. Experimental results also demonstrate the effectiveness of this method. Through the simulation experiment, it can be seen that regardless of the goal of uniform motion or motion with variable velocity, DESO motion prediction based on the template matching algorithm can accurately predict the trajectory of the target, and does not depend on the target motion model. The use of DESO is simple, the transition time is short and the tracking performance is satisfying.

[1]  Kanad K. Biswas,et al.  Gesture recognition using Microsoft Kinect® , 2011, The 5th International Conference on Automation, Robotics and Applications.

[2]  Rogério Schmidt Feris,et al.  Wavelet Subspace Method for Real-Time Face Tracking , 2001, DAGM-Symposium.

[3]  Harry Wechsler,et al.  Recognition of arm movements , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[4]  Kin Fun Li,et al.  A Web-Based Sign Language Translator Using 3D Video Processing , 2011, 2011 14th International Conference on Network-Based Information Systems.

[5]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[6]  Liviu Goras,et al.  On HMM static hand gesture recognition , 2011, ISSCS 2011 - International Symposium on Signals, Circuits and Systems.

[7]  Dan Ionescu,et al.  Multimodal control of virtual game environments through gestures and physical controllers , 2011, 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings.

[8]  Junsong Yuan,et al.  Robust hand gesture recognition with kinect sensor , 2011, ACM Multimedia.

[9]  Jong-wook Kang,et al.  A Study on the control Method of 3-Dimensional Space Application using KINECT System , 2011 .

[10]  Mikhail J. Atallah.  Faster image template matching in the sum of the absolute value of differences measure , 2001, IEEE Trans. Image Process..

[11]  Albert A. Rizzo,et al.  Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[13]  Maria de F. Q. V. Turnell,et al.  Virtual reality training environment a proposed architecture , 2010, 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems.

[14]  Zhipei Huang,et al.  Human modeling and real-time motion reconstruction for micro-sensor motion capture , 2011, 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings.

[15]  Paul M. Fitts,et al.  Perceptual-Motor Skill Learning1 , 1964 .

[16]  Vassilis Athitsos,et al.  Comparing gesture recognition accuracy using color and depth information , 2011, PETRA '11.