A Survey on Object Detection in Dynamic and Complex Environments

Object detection has become a cornerstone of computer vision, with applications ranging from autonomous driving and robotics to surveillance and augmented reality. While substantial progress has been made in controlled and static settings, real-world environments often pose significant challenges due to dynamic backgrounds, occlusions, illumination variations, and cluttered scenes. This survey provides a comprehensive review of recent advancements in object detection specifically tailored for dynamic and complex environments. We classify existing approaches based on their core methodologies, including traditional feature-based techniques, deep learning models, and hybrid frameworks. Key challenges such as real-time performance, adaptability to environmental changes, and robustness to motion are discussed in depth. Furthermore, we analyze benchmark datasets and evaluation metrics commonly used in this domain, highlighting their limitations and suggesting improvements. Finally, we explore emerging trends and future directions, including the integration of spatiotemporal modeling, sensor fusion, and domain adaptation strategies. This survey aims to serve as a valuable reference for researchers and practitioners seeking to develop or apply object detection systems in real-world, unpredictable environments.