Year: 2023
Type: preprint
Abstract: Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches. Additionally, we present a comparative analysis of the methods and highlight their strengths and weaknesses. Finally, we provide some future research directions to further improve object detection in unconstrained environments.
Source: arXiv (Cornell University)
Author: Patel, Hrishitva
Institution: The University of Texas at San Antonio
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4365211626