Background Reconstruction

Background reconstruction aims to isolate and remove background elements from images or video sequences, leaving only the foreground objects of interest. Current research focuses on improving the accuracy and robustness of this process, particularly in challenging scenarios like complex urban scenes or infrared imagery, employing techniques such as neural networks (including autoencoders and transformers) and incorporating semantic information or motion cues to enhance performance. These advancements have significant implications for various applications, including object detection, video surveillance, and autonomous driving, by enabling more reliable and efficient analysis of dynamic scenes.