In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion (This dataset can be downloaded from http://cs.nyu.edu/∼ajain/accv2014/.), that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.
CITATION STYLE
Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2015). MoDeep: A deep learning framework using motion features for human pose estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9004, pp. 302–315). Springer Verlag. https://doi.org/10.1007/978-3-319-16808-1_21
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