Abstract
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.
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References
Toh, V., Gray, A. J., Knight, C. H., & Glasbey, C. A. (2003). Comparing colour space models and pattern recognition techniques for segmentation of mammary tissue images. In International Conference on Visual Information Engineering, 2003. VIE 2003.
Ye, Z., Mohamadian, H., & Ye, Y. (2011). Comparative study of linear and nonlinear colour model identification based optimal feature extraction. In 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI).
Liu, C., & Yang, J. (2009). ICA color space for pattern recognition. IEEE Transactions on Neural Networks, 20(2).
Lee, S. H., Choi, J. Y., Ro, Y. M., & Plataniotis, K. N. (2012). Local color vector binary patterns from multichannel face images for face recognition. IEEE Transactions on Image Processing, 21(4).
Castelnovi, M., Musso, P., Sgorbissa, A., & Zaccaria, R. (2003). Surveillance robotics: Analyzing scenes by colors analysis and clustering. In 2003 Computational Intelligence in Robotics and Automation. Proceedings.
Pribula, V., & Canosa, R. L. (2013). LBP-inspired detection of color patterns: Multiplied local score patterns. In Image Processing Workshop (WNYIPW). New York: IEEE Western
Saigaa, D., Fedias, M., Harrag, A., Bouchelaghem, A., & Drif, M. (2011). Color space MS-based feature extraction method for face verification. In 2011 11th International Conference on Hybrid Intelligent Systems (HIS).
Deb, K., Lim, H., & Jo, K.-H. (2009). Vehicle license plate extraction based on color and geometrical features. In IEEE International Symposium on Industrial Electronics. ISIE 2009.
Mindru, F., Moons, T., & Van Gool, L. (1999). Recognizing color patterns irrespective of viewpoint and illumination. In Computer Vision and Pattern Recognition.
Choi, J. Y., Ro, Y. M., & Plataniotis, K. N. (2012). Color local texture features for color face recognition. IEEE Transactions on Image Processing, 21(3)
Arivazhagan, S., & Benitta, R. (2013). Texture classification using colour local texture features. In 2013 International Conference on Signal Processing Image Processing & Pattern Recognition (ICSIPR).
Porebski, A., Vandenbroucke, N., & Macaire, L. (2007). Iterative feature selection for color texture classification. In IEEE International Conference on Image Processing, 2007. ICIP 2007 (Vol. 3).
Ko, T. (2006). Viewpoint-invariant and illumination-invariant classification of natural surfaces using general-purpose color and texture features with the ALISA dCRC classifier. In Applied Imagery and Pattern Recognition Workshop, 2006. AIPR 2006. 35th IEEE.
Wakahara, T. (2008). Figure-ground discrimination and distortion-tolerant recognition of color characters in scene images. In 19th International Conference on Pattern Recognition, 2008. ICPR 2008.
Jedrasiak, K., Bereska, D., & Nawrat, A. (2013). The prototype of gyro-stabilized UAV gimbal for day-night surveillance. Advanced technologies for intelligent systems of national border security (pp. 107–115). Berlin, Heidelberg: Springer.
Ryt, A., Sobel, D., Kwiatkowski, J., Domzal, M., Jedrasiak, K., & Nawrat, A. (2014). Real-time laser point tracking. In International Conference on Computer Vision and Graphics (pp. 542–551). Springer International Publishing.
Daniec, K., Iwaneczko, P., Jedrasiak, K., & Nawrat, A. (2013). Prototyping the Autonomous Flight Algorithms Using the Prepar3Dő Simulator. In Vision based systems for UAV applications (pp. 219–232). Springer International Publishing.
Nawrat, A. (2008). Jedrasiak (pp. 69–76). Fast colour recognition algorithm for robotics, Problemy Eksploatacji: K.
Sobel, D., Jedrasiak, K., Daniec, K., Wrona, J., Jurgas, P., & Nawrat, A. (2014). Camera calibration for tracked vehicles augmented reality applications. In Innovative control systems for tracked vehicle platforms (pp. 147–162). Springer International Publishing.
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Kuś, Z., Cymerski, J., Radziszewska, J., Nawrat, A. (2018). Applying Colour Image-Based Indicator for Object Tracking. In: Nawrat, A., Bereska, D., Jędrasiak, K. (eds) Advanced Technologies in Practical Applications for National Security. Studies in Systems, Decision and Control, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-64674-9_2
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DOI: https://doi.org/10.1007/978-3-319-64674-9_2
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