Abstract
Tracking and segmentation of moving objects in videos continues to be the central problem in the separation and prediction of concurrent episodes and situation understanding. Along with critical issues such as collision avoidance, tracking and segmentation have numerous applications in other disciplines, including medicine research. To infer the potential side effects of a given treatment, behaviour analysis of la-boratory animals should be performed, which can be achieved via tracking. This presents a difficult task due to the special circumstances, such as the highly similar shape and the unpredictable movement of the subject creatures, but a precise solution would accelerate research by eliminating the need of manual supervision. To this end, we propose Cluster R-CNN, a deep architecture that uses clustering to segment object instances in a given image and track them across subsequent frames. We show that pairwise clustering coupled with a recurrent unit successfully extends Mask R-CNN to a model capable of tracking and segmenting highly similar moving and occluded objects, providing proper results even in certain cases where related networks fail. In addition to theoretical background and reasoning, our work also features experiments on a unique rat tracking data set, with quantitative results to compare the aforementioned model with other architectures. The proposed Cluster R-CNN serves as a baseline for future work towards achieving an automatic monitoring tool for biomedical research.
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Acknowledgements
We thank Bence D. Szalay for his careful help in composing this paper; his work was supported by the Hungarian Government and co-financed by the European Social Fund (EFOP-3.6.3-VEKOP-16-2017-00002: EFOP-3.6.3-VEKOP-16-2017-00002, Integrated Program for Training New Generation of Scientists in the Fields of Computer Science). We also thank Árpád Dobolyi and Dávid Keller for providing the database. ÁF and AL were supported by the ELTE Institutional Excellence Program of the National Research, Development and Innovation Office (NKFIH-1157-8/2019-DT) and by the Thematic Excellence Programme (Project no. ED_18-1-2019-0030 titled Application-specific highly reliable IT solutions) of the National Research, Development and Innovation Fund of Hungary, respectively.
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Fóthi, Á., Faragó, K.B., Kopácsi, L., Milacski, Z.Á., Varga, V., Lőrincz, A. (2020). Multi Object Tracking for Similar Instances: A Hybrid Architecture. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_37
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