Applying Colour Image-Based Indicator for Object Tracking

The goal of the following paper was to devise a method of object tracking with the application of the tracked objects’ coloured images. The proposed solution was based on the calculation of an indicator which described the colour features of the object. The ratio between the red and green components as well as the ratio between red and blue components were the indicators which defined these features. Moreover, the proposed approach was particularly useful in the cases when the object and terrain colours were significantly different. The abovementioned ratios were used to create the pattern vector. Such a defined pattern vector was used to calculate the error function and the minimum of this function indicated the object location. This paper presented the examples of object tracking for both: the different object colour from the terrain colour and the similar object colour to the terrain colour.

[1]  Dawid Sobel,et al.  Camera Calibration for Tracked Vehicles Augmented Reality Applications , 2014 .

[2]  C. A. Glasbey,et al.  Comparing colour space models and pattern recognition techniques for segmentation of mammary tissue images , 2003 .

[3]  Alice Porebski,et al.  Iterative Feature Selection for Color Texture Classification , 2007, 2007 IEEE International Conference on Image Processing.

[4]  Yong Man Ro,et al.  Color Local Texture Features for Color Face Recognition , 2012, IEEE Transactions on Image Processing.

[5]  Kang-Hyun Jo,et al.  Vehicle license plate extraction based on color and geometrical features , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[6]  Karol Jędrasiak,et al.  The Prototype of Gyro-Stabilized UAV Gimbal for Day-Night Surveillance , 2013 .

[7]  Zhengmao Ye,et al.  Comparative study of linear and nonlinear color model identification based optimal feature extraction , 2011, 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI).

[8]  Roxanne L. Canosa,et al.  LBP-inspired detection of color patterns: Multiplied local score patterns , 2013, 2013 IEEE Western New York Image Processing Workshop (WNYIPW).

[9]  Renato Zaccaria,et al.  Surveillance robotics: analyzing scenes by colors analysis and clustering , 2003, Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694).

[10]  Chengjun Liu,et al.  ICA Color Space for Pattern Recognition , 2009, IEEE Transactions on Neural Networks.

[11]  Peter Bock,et al.  Viewpoint-Invariant and Illumination-Invariant Classification of Natural Surfaces Using General-Purpose Color and Texture Features with the ALISA dCRC Classifier , 2006, 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06).

[12]  S. Arivazhagan,et al.  Texture classification using color local texture features , 2013, 2013 International Conference on Signal Processing , Image Processing & Pattern Recognition.

[13]  Artur Ryt,et al.  Real-Time Laser Point Tracking , 2014, ICCVG.

[14]  Karol Jędrasiak,et al.  Fast colour recognition algorithm for robotics , 2008 .

[15]  Aleksander Nawrat,et al.  Prototyping the Autonomous Flight Algorithms Using the Prepar3D® Simulator , 2013, Vision Based Systemsfor UAV Applications.

[16]  Luc Van Gool,et al.  Recognizing color patterns irrespective of viewpoint and illumination , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[17]  Toru Wakahara Figure-ground discrimination and distortion-tolerant recognition of color characters in scene images , 2008, 2008 19th International Conference on Pattern Recognition.

[18]  M. Drif,et al.  Color space MS-based feature extraction method for face verification , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[19]  Yong Man Ro,et al.  Local Color Vector Binary Patterns From Multichannel Face Images for Face Recognition , 2012, IEEE Transactions on Image Processing.