Color Objects Detection in Real-Time with Raspberry Pi and Image Processing
Main Article Content
Abstract
Color has been broadly utilized continuously in many applications. It offers a few massive focal points over geometric prompts, for example, computational straightforwardness, heartiness under partial impediment, pivot, scale, and goal changes. Even though color techniques end up being proficient in an assortment of computer vision applications, there are a few issues related to these strategies for which color tracking is one of the most significant. Color has been generally utilized in machine-based vision frameworks for assignments, for example, picture division, object tracking, and detection. The objective of this task is the proficient following of tracking color objects from a grouping of live pictures for use continuously applications including observation and robot route. This paper addresses building a real-time tracking framework comprising standard equipment and fit for tracking various objects from an edge arrangement progressively.
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How to Cite
Chakravarthy, K. V., & Krishna, M. V. (2021). Color Objects Detection in Real-Time with Raspberry Pi and Image Processing. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology, 13(01), 5-7. https://doi.org/10.18090/samriddhi.v13i01.2
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Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
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[2] Vladimir Kravtchenko . (1999). Tracking Color Objects in Real Time The University of British Columbia, August 27,123-126.
[3] McAndrew A. (2004). An introduction to digital image was processing with matlab notes for scm2511 image processing,” School of Computer Science and Mathematics, Victoria University of Technology.
[4] D. Senthamaraikannan,S. Shriram, Dr. J. William,(2014). Real Time Color Recognition,“ International Journal of Innovative Research In Electrical, Electronics, Instrumentation and Control Engineering Vol. 2, Issue 3.
[5] Rani NS, Neethu OP & Ponnath N. (2015). Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing, International Journal of Electrical and Computer Engineering, Vol.5, No.4.
[6] Nalluri, S. K., & Parasaram, V. K. B. (2015). Automating Software Builds with Jenkins: Design Patterns and Failure Handling. International Journal of Technology, Management and Humanities, 1(01), 16-33. https://doi.org/10.21590/ijtmh.01.02.03
[7] Rani NS, Verma SK & Joseph A. (2016). A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters, International Journal of Electrical and Computer Engineering, Vol.6, Issue 4, PP 114–118.
[8] Kalisa Wilson.(2016) .International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.9, No.4, (2016), pp.331– 338.
[9] A. Pan Thu Tun, B. Khine Myint. (2016). MonColor Paper Detection System, IOSRJournalof Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278–8735.
Volume 11, Issue 5, Ver. I (Sep.-Oct), PP 110–114
[10] Arshi Prabhakar, Neeti and Rakhi Devi. (2017).Di erent Color Detection In an RGB Image, International Journal of Development Research ‘Vol. 07, Issue, 08, pp.14503–14506.
[11] P. Sudharshan Duth, M. Mary Deepa. (2018). Color detection in RGB modeled images using MAT LAB,” International Journal of Engineering & Technology, 7 (2.31), 29–33 .