Fine-Tuning Human Pose Estimation in Videos
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 gain over the baseline on our new dataset of unrestricted sports videos. The self-training model presented has two components: a static Pictorial Structure 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.
Here we release our implementation of  for MATLAB software. To read more about the method, check the pdf on the left.
|fine_tuning_pose.tar.gz||Matlab code for fine-tuning human pose estimation in videos.||94 MB|
|README||Description on running the code and other info.||4.0 KB|
|cvit_sports_videos.tar.gz||CVIT-SPORTS-Videos dataset of 11 video sequences from cricket domain.||66 MB|
 Y. Yang, D. Ramanan. Articulated Pose Estimation using Flexible Mixtures of Parts. CVPR 2011.
 A. Cherian, J. Marial, K. Alahari, C. Schmid. Mixing Body-Part Sequences for Human Pose Estimation. CVPR 2014.
 B. Sapp, D. Weiss, B. Taskar. Parsing Human Motion with Stretchable Models. CVPR 2011.
 T. Malisiewicz, A. Gupta, A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond. ICCV 2011.