Fast Covariance Matching With Fuzzy Genetic Algorithm

The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To avoid premature convergence or local optimum which often occur in traditional GAs, we use a fuzzy inference system to adaptively estimate the crossover and mutation probabilities to gain convergence in a much higher speed than using a conventional GA. Experimental results show that the proposed approach can significantly improve the processing speed of covariance matching, while keeping the matching results almost unchanged. The runtime performance of the proposed approach is faster than its counterparts using exhaustive search with eight times and more.

[1]  Honghai Liu,et al.  Fuzzy Qualitative Human Motion Analysis , 2009, IEEE Transactions on Fuzzy Systems.

[2]  Damian M. Lyons,et al.  Combining multiple scoring systems for target tracking using rank-score characteristics , 2009, Inf. Fusion.

[3]  Adam Slowik,et al.  Steering of Balance between Exploration and Exploitation Properties of Evolutionary Algorithms - Mix Selection , 2010, ICAISC.

[4]  Gerrit Kateman,et al.  Optimization of calibration data with the dynamic genetic algorithm , 1992 .

[5]  Honghai Liu,et al.  Target tracking for mobile robot platforms via object matching and background anti-matching , 2010, Robotics Auton. Syst..

[6]  Honghai Liu,et al.  Advances in View-Invariant Human Motion Analysis: A Review , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Fatih Murat Porikli,et al.  Fast Construction of Covariance Matrices for Arbitrary Size Image Windows , 2006, 2006 International Conference on Image Processing.

[8]  Xuelong Li,et al.  Gabor-Based Region Covariance Matrices for Face Recognition , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Kresimir Simunic.  A Genetic Search-based Partial Image Matching , 1998 .

[10]  A. T. Johns,et al.  Fuzzy logic controlled genetic algorithms , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[11]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[12]  Abdullah Bal,et al.  Improved Multiple Target Tracking via Global Motion Compensation and Optoelectronic Correlation , 2007, IEEE Transactions on Industrial Electronics.

[13]  Tieniu Tan,et al.  A real-time object detecting and tracking system for outdoor night surveillance , 2008, Pattern Recognit..

[14]  H J Song,et al.  Fast image template matching based on evolutionary algorithms , 2004 .

[15]  Hong Bao,et al.  GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia , 2010, IEEE Transactions on Information Technology in Biomedicine.

[16]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[17]  Honghai Liu,et al.  Fuzzy Qualitative Robot Kinematics , 2008, IEEE Transactions on Fuzzy Systems.

[18]  Mayez A. Al-Mouhamed,et al.  A robust gross-to-fine pattern recognition system , 2001, IEEE Trans. Ind. Electron..

[19]  Lizhe Wang,et al.  Massively Parallel Neural Signal Processing on a Many-Core Platform , 2011, Computing in Science & Engineering.

[20]  Menouar Boulif,et al.  A new fuzzy genetic algorithm for the dynamic bi-objective cell formation problem considering passive and active strategies , 2008, Int. J. Approx. Reason..

[21]  Stephen John Turner,et al.  journal homepage: www.elsevier.com/locate/jpdc Synchronization in federation community networks , 2022 .

[22]  E. H. K. Fung,et al.  Intelligent Automatic Fault Detection for Actuator Failures in Aircraft , 2009, IEEE Transactions on Industrial Informatics.

[23]  Mario Luca Fravolini,et al.  Predictive reference shaping for constrained robotic systems using evolutionary algorithms , 2003, Appl. Soft Comput..

[24]  Lindsay Kleeman,et al.  Fusion of multimodal visual cues for model-based object tracking , 2003 .

[25]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Jorge S. Marques,et al.  Robust shape tracking in the presence of cluttered background , 2000, IEEE Transactions on Multimedia.

[27]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  W. Förstner,et al.  A Metric for Covariance Matrices , 2003 .

[29]  Fatih Murat Porikli,et al.  Covariance Tracking using Model Update Based on Lie Algebra , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[30]  Witold Pedrycz,et al.  Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems , 2009, IEEE Transactions on Industrial Informatics.

[31]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[32]  Long Wang,et al.  Vision-Based Target Tracking and Collision Avoidance for Two Autonomous Robotic Fish , 2009, IEEE Transactions on Industrial Electronics.

[33]  Mitsuo Gen,et al.  Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling , 2003, Appl. Soft Comput..