Abstract
Tracking and body pose estimation of clinical staff have several applications in the analysis of surgical workflow, such as radiation monitoring, surgical activity recognition and the study of ergonomics. The operating room is, however, a very complex environment for visual tracking due to frequent illumination changes, clutter, similar color of clinicians’ scrubs and limited sensor positioning. Furthermore, several applications, such as radiation monitoring, require consistent and accurate body part tracking over defined periods of time, which is a challenging task in the aforementioned conditions. In this paper, we tackle the problem of pose estimation in the interventional room. We also propose a method to consistently track upper body parts in short sequences by using RGBD data and discrete Markov Random Field (MRF) optimization over the complete set of frames. The proposed MRF energy formulation enforces both body kinematic and temporal constraints in order to cope with the natural ambiguities of tracking and with the frequent failure of the underlying depth-based body part detector in such conditions. We evaluate our approach quantitatively on seven manually-annotated sequences recorded in the interventional room and show that it can consistently track the upper-body of persons present in the room.
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Kadkhodamohammadi, A., Gangi, A., de Mathelin, M., Padoy, N. (2014). Temporally Consistent 3D Pose Estimation in the Interventional Room Using Discrete MRF Optimization over RGBD Sequences. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham. https://doi.org/10.1007/978-3-319-07521-1_18
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DOI: https://doi.org/10.1007/978-3-319-07521-1_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07520-4
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