OBJECT RECOGNITION FROM VIDEO SEQUENCES IN REAL TIME USING DEEP LEARNING TECHNIQUES
Keywords:Objects Recognition, Deep Learning, Computer Vision
AbstractIn the field of computer vision, a wide range of applications has been developed for various purposes. Object recognition in images has always been a relevant task in this application domain. Nowadays, new methods have been developed to achieve greater accuracy and efficiency in the process of object detection. In this research, a comparative analysis of the main methods based on deep learning was carried out, which are among the most effective for object detection and classification. It was proposed an implementation based on the YOLO algorithm for object recognition in video sequences, which uses the library for computer vision OpenCV. Experiments designed to validate the proposed solution were performed on two different computation nodes and the results obtained were analyzed according to the main metrics employed to evaluate the performance of object recognition methods.
Download data is not yet available.
How to Cite
García Cotrina, E., Hernández Duany, O. A., & García Albert, A. (2021). OBJECT RECOGNITION FROM VIDEO SEQUENCES IN REAL TIME USING DEEP LEARNING TECHNIQUES. Telemática, 20(2). Retrieved from https://revistatelematica.cujae.edu.cu/index.php/tele/article/view/459
The authors who publish in this journal agree to the following terms:
- The authors retain the copyright and guarantee to the journal the right to be the first publication of the work are distributed under a license of use and distribution "Creative Commons Attribution-NonCommercial-NoDerivativeWorks 3.0 Unported" (CC BY-NC-ND 3.0) You can consult from here the informative version and the legal text of the license that allows others to share the work with an acknowledgement of the authorship of the work and the initial publication in this journal.
- Authors may separately enter into additional agreements for non-exclusive distribution of the version of the work published in the journal (for example, placing it in an institutional repository or publishing it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are allowed and encouraged to disseminate their work electronically (e.g., in institutional repositories or on their own website) before and during the submission process, as this can lead to productive exchanges as well as earlier and greater citation of published work (see The Effect of Open Access).