Fine-tuning human pose estimations in videos

Singh, D and Balasubramanian, Vineeth N and C V, Jawahar (2016) Fine-tuning human pose estimations in videos. In: IEEE Winter Conference on Applications of Computer Vision (WACV), 7-10 March 2016, Lake Placid, NY.

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We propose a semi-supervised self-training method for fine-tuning human pose estimations in videos that provides accurate estimations even for complex sequences. We surpass state-of-the-art on most of the datasets used and also show a 2.33% gain over the baseline on our new dataset of unrestricted sports videos. The self-training model presented has two components: a static Pictorial Structure (PS) based model and a dynamic ensemble of exemplars. We present a pose quality criteria that is primarily used for batch selection and automatic parameter selection. The same criteria works as a low-level pose evaluator used in post-processing. We set a new challenge by introducing a full human body-parts annotated complex dataset, CVIT-SPORTS, which contains complex videos from the sports domain. The strength of our method is demonstrated by adapting to videos of complex activities such as cricket-bowling, cricket-batting, football as well as available standard datasets.

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IITH Creators:
IITH CreatorsORCiD
Balasubramanian, Vineeth NUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Computational modeling Data models, Tracking, Videos, Wrist
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 04 Aug 2016 05:21
Last Modified: 25 Apr 2018 05:41
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