Abstract
While detecting object becomes easier with deep models, estimating pose remains a challenging problem in modern vision research. In this work, we propose a method that enables detecting objects and estimating their pose simultaneously in a single model, without intermediate stages. Unlike some other approaches, we make the first attempt to hierarchically estimate objects pose using a deep network. We approach the problem by trying to mimic human perception in seeing objects, as we tend to estimate the object pose based on our experience and usually from coarse to fine fashion. We with enough resource to build a deeper model, our approach will be able to produce more accurate results for to complex tasks including object tracking, localization, and SLAM [2].
Q. A. Dang and Q. M. B. Nguyen—Contributed equally to this manuscript.
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Dang, Q.A., Nguyen, Q.M.B., Nguyen, D.D. (2019). Azimuth Angle Detection with Single Shot MultiBox Detecting Model. In: Lee, S., Ismail, R., Choo, H. (eds) Proceedings of the 13th International Conference on Ubiquitous Information Management and Communication (IMCOM) 2019. IMCOM 2019. Advances in Intelligent Systems and Computing, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-19063-7_31
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