Unsupervised construction of task-specific datasets for object re-identification

0548651 - ÚI 2022 RIV US eng C - Conference Paper (international conference)
Pulc, P. - Holeňa, Martin
Unsupervised construction of task-specific datasets for object re-identification.
ICCTA 2021: 2021 7th International Conference on Computer Technology Applications. 2021 Proceedings. New York: Association for Computing Machinery, 2021, s. 66-72. ACM International Conference Proceeding Series. ISBN 978-1-4503-9052-1.
[ICCTA 2021: International Conference on Computer Technology Applications /7./. Vienna / Online (AT), 13.07.2021-15.07.2021]
R&D Projects: GA ČR(CZ) GA18-18080S
Grant - others:Ministerstvo školství, mládeže a tělovýchovy - GA MŠk(CZ) LM2018140
Institutional support: RVO:67985807
Keywords : Fine-tuning of Object Re-identification * Multiple Object Tracking * Hierarchical Sparse Feature Tracking
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

In the last decade, we have seen a significant uprise of deep neural networks in image processing tasks and many other research areas. However, while various neural architectures have successfully solved numerous tasks, they constantly demand more and more processing time and training data. Moreover, the current trend of using existing pre-trained architectures just as backbones and attaching new processing branches on top not only increases this demand but diminishes the explainability of the whole model. Our research focuses on combinations of explainable building blocks for the image processing tasks, such as object tracking. We propose a combination of Mask R-CNN, state-of-the-art object detection and segmentation neural network, with our previously published method of sparse feature tracking [16]. Such a combination allows us to track objects by connecting detected masks using the proposed sparse feature tracklets. However, this method cannot recover from complete object occlusions and has to be assisted by an object re-identification. To this end, this paper uses our feature tracking method for a slightly different task: an unsupervised extraction of object representations that we can directly use to fine-tune an object re-identification algorithm, see Fig. 1 for visualisation. As we have to use objects masks already in the object tracking, our approach utilises the additional information as an alpha channel of the object representations, which further increases the precision of the re-identification. An additional benefit is that our fine-tuning method can be employed even in a fully online scenario.
Permanent Link: http://hdl.handle.net/11104/0324702