Intelligent control based on wavelet decomposition and neural network for predicting of human trajectories with a novel vision-based robotic

Abstract In this paper, an intelligent novel vision-based robotic tracking model is developed to predict the performance of human trajectories with a novel vision-based robotic tracking system. The developed model is based on wavelet packet decomposition, entropy and neural network. We represent an implementation of a novel vision-based robotic tracking system based on wavelet decomposition and artificial neural (WD-ANN) which can track desired human trajectory pattern in real environments. The input–output data set of the novel vision-based robotic tracking system were first stored and than these data sets were used to predict the robotic tracking based on WD-ANN. In simulations, performance measures were obtained to compare the predicted and human–robot trajectories like actual values for model validation. In statistical analysis, the RMS value is 0.0729 and the R2 value is 99.76% for the WD-ANN model. This study shows that the values predicted with the WD-ANN can be used to predict human trajectory by vision-based robotic tracking system quite accurately. All simulations have shown that the proposed method is more effective and controls the systems quite successful.

[1]  T. Mexia,et al.  Author ' s personal copy , 2009 .

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[5]  Nong Sang,et al.  Improved mean shift algorithm for occlusion pedestrian tracking , 2008 .

[6]  M. Smids,et al.  Background Subtraction for Urban Traffic Monitoring using Webcams , 2007 .

[7]  Yunong Zhang,et al.  Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primal-dual neural network , 2009 .

[8]  Mehmet Karaköse,et al.  Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system , 2009, Expert Syst. Appl..

[9]  Emil M. Petriu,et al.  Three-Dimensional Head Tracking and Facial Expression Recovery Using an Anthropometric Muscle-Based Active Appearance Model , 2008, IEEE Transactions on Instrumentation and Measurement.

[10]  Thorsten Schmitt,et al.  Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario , 1999, RoboCup.

[11]  Afsaneh Alavi Naini,et al.  Face detection based on dimension reduction using probabilistic neural network and Genetic Algorithm , 2009, 2009 6th International Symposium on Mechatronics and its Applications.

[12]  Hyung-Il Choi,et al.  Active models for tracking moving objects , 2000, Pattern Recognit..

[13]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[14]  Richard Szeliski,et al.  A layered video object coding system using sprite and affine motion model , 1997, IEEE Trans. Circuits Syst. Video Technol..

[15]  R Quian Quiroga,et al.  Wavelet entropy: a measure of order in evoked potentials. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[16]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[17]  Ming-Chieh Lee,et al.  Semiautomatic segmentation and tracking of semantic video objects , 1998, IEEE Trans. Circuits Syst. Video Technol..

[18]  Ramesh A. Gopinath,et al.  Wavelets and Wavelet Transforms , 1998 .

[19]  Zhiping Lin,et al.  Predicting time series with wavelet packet neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[20]  JongWon Kim,et al.  An interactive object segmentation system for MPEG video , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[21]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[22]  Munchurl Kim,et al.  Moving object segmentation in video sequences by user interaction and automatic object tracking , 2001, Image Vis. Comput..

[23]  Rainer Stiefelhagen,et al.  Multi-view head pose estimation using neural networks , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[24]  J. Castagna,et al.  Framework for AVO gradient and intercept interpretation , 1998 .

[25]  Ahmet Arslan,et al.  An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks , 2003, Comput. Biol. Medicine.

[26]  Manuela M. Veloso,et al.  Fast and inexpensive color image segmentation for interactive robots , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[27]  Stefan Wermter,et al.  Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks , 2009, Neural Networks.

[28]  Shih-Fu Chang,et al.  Digital image/video library and MPEG-7: standardization and research issues , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[29]  Servet Soyguder,et al.  Predicting of fan speed for energy saving in HVAC system based on adaptive network based fuzzy inference system , 2009, Expert Syst. Appl..

[30]  Gernot A. Fink,et al.  Face Detection Using GPU-Based Convolutional Neural Networks , 2009, CAIP.

[31]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[32]  Suresh R. Devasahayam,et al.  Signals and systems in biomedical engineering , 2000 .

[33]  Servet Soyguder,et al.  An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach , 2009 .

[34]  Tin Hninn Hninn Maung,et al.  Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks , 2009 .