This thesis addresses the problem of visual tracking of objects within images captured by smart bins to determine whether an object is new or has already been observed. Unlike traditional object detection methods, where each instance is recognized independently, this work focuses on the temporal dimension and the system's ability to retain the memory of previously seen objects, thus improving continuous and automated waste monitoring. The dataset used, consisting of approximately 7,000 images annotated in Common Object in Context (COCO) format, includes bounding boxes, segmentation masks, object categories (about 60 classes), and a custom "new" attribute indicating whether an object is new with respect to the temporal context.