Version 1
: Received: 9 February 2024 / Approved: 9 February 2024 / Online: 9 February 2024 (11:45:38 CET)
How to cite:
CHIDI, E. U.; UDANOR, C. N.; ANOLIEFO, E. Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints2024, 2024020583. https://doi.org/10.20944/preprints202402.0583.v1
CHIDI, E. U.; UDANOR, C. N.; ANOLIEFO, E. Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints 2024, 2024020583. https://doi.org/10.20944/preprints202402.0583.v1
CHIDI, E. U.; UDANOR, C. N.; ANOLIEFO, E. Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints2024, 2024020583. https://doi.org/10.20944/preprints202402.0583.v1
APA Style
CHIDI, E. U., UDANOR, C. N., & ANOLIEFO, E. (2024). Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques. Preprints. https://doi.org/10.20944/preprints202402.0583.v1
Chicago/Turabian Style
CHIDI, E. U., COLLINS N. UDANOR and EDWARD ANOLIEFO. 2024 "Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques" Preprints. https://doi.org/10.20944/preprints202402.0583.v1
Abstract
Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.
Keywords
Object detection, object of interest detection, object of instance detection, deep learning.
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
