Fast re-OBJ: real-time object re-identification in rigid scenes - Bayraktar, Ertugrul; Wang, Yiming; DelBue, Alessio (SPRINGER, 2022)
Re-identifying objects in a rigid scene across varying viewpoints (object Re-ID) is a challenging task, in particular when there are similar, even identical objects coexist in the same environment. Discriminative features play no doubt an essential role in addressing this challenge, while for practical deployment, real-time performance is another desired attribute. We therefore propose a novel framework, named Fast re-OBJ, that is able to improve both Re-ID accuracy and processing speed via tight coupling between the instance segmentation module and embedding generation module. The rich object encoding in the instance segmentation backbone is directly shared to the embedding generation module for training a more discriminative representation via a triplet network. Moreover, we create datasets with the segmentation outputs using real-time object detectors to train and evaluate our object embedding module. With extensive experiments, we prove that our proposed Fast re-OBJ improves the object Re-ID accuracy by 5% and the speed is 5 x faster compared to the state-of-the-art methods. The dataset and code repository are publicly available at: https://tinyurl.com/bdsb53c4.
Keywords: Object re-identification; Embedding; Triplet network; Real-time; Ranking; Computer Science; Engineering