A Comprehensive Study on Object Detection Techniques in Unconstrained Environments
It provides a review for researchers in computer vision, but is incremental as it synthesizes existing knowledge without introducing new methods.
This paper conducts a comprehensive study on object detection techniques in unconstrained environments, analyzing challenges, datasets, and state-of-the-art approaches, but does not report specific experimental results or numbers.
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.