Abstract
In this paper we introduce an enhanced multi-person pose estimation method for the competition of the PoseTrack [6] workshop in ECCV 2018. We employ a two-stage human pose detector, where human region detection and keypoint detection are separately performed. A strong encoder-decoder network for keypoint detection has achieved 70.4% mAP for PoseTrack 2018 validation dataset.
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CITATION STYLE
Honda, H., Kato, T., & Uchida, Y. (2019). Enhanced two-stage multi-person pose estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11130 LNCS, pp. 217–220). Springer Verlag. https://doi.org/10.1007/978-3-030-11012-3_18
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