Robust Object Recognition in Adverse Environments

Abstract

This paper presents a novel approach for moving object recognition utilizing the Python programming language and the OpenCV computer vision library. Moving object recognition holds significant importance in various domains, including surveillance, robotics, and traffic monitoring. Despite existing methodologies, achieving accurate and real-time recognition remains a challenge due to complexities in dynamic environments. Our plan solves this problem challenge by leveraging Python's versatility and OpenCV's robust features. We introduce a multi-stage process that also includes the background subtraction, feature extraction, and also object tracking algorithms to accurately recognising and tracking moving objects in real-time video streams. Additionally, we incorporate optimization techniques to enhance computational efficiency without compromising accuracy. Experimental results show the effectiveness of our approach, showcasing improved performance compared to existing methods. The proposed solution offers promising implications for real-world applications, including enhanced surveillance systems and intelligent transportation systems, and augmented reality environments.