Detecting and Tracking Objects in Challenging Weather Conditions Using Advanced Models
What is it about?
The paper presents an advanced model for detecting and tracking objects in adverse weather conditions. It proposes the use of TSM-EFFICIENTDET and JS-KM with Pearson-Retinex to address the challenges posed by weather conditions such as mist, fog, and haze. The model aims to enhance object detection and tracking accuracies under adverse weather conditions by employing innovative techniques and algorithms.
Why is it important?
The research is important because it addresses a critical challenge in computer vision and surveillance systems. Adverse weather conditions can significantly impact the accuracy of object detection and tracking in video sequences. By proposing an advanced model that specifically targets these challenges, the research aims to improve the reliability and effectiveness of object detection and tracking systems, particularly in real-world scenarios where adverse weather conditions can interfere with traditional methodologies. This has implications for various applications, including surveillance, autonomous vehicles, and robotics, where accurate object detection and tracking are essential.