Long-term tracking of human pose in videos

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• Dr James Charles, Research Fellow at the University of Leeds: Computer Vision - Machine Learning - Probabilistic modeling
• Wednesday 10 February 2016, 11:00-12:00
• Cambridge University Engineering Department, Oatley Meeting Room (Room No. BN2-05).

If you have a question about this talk, please contact gd348.

This talk presents random forest and convolutional neural network (ConvNet) based methods for real-time human pose estimation in videos. I will show the proposed methods accurately track the 2D position of upper body joints, such as the wrists and elbows, despite fast motion and continuously changing cluttered background. The main focus of this talk is the introduction of my recently developed personalised ConvNet pose estimation method, which greatly outperforms the current state-of-the-art on numerous video datasets. Furthermore, I will introduce a new challenging video pose dataset collected from YouTube, and show how we can generate copious amounts of labelled pose training data efficiently and fully automatically.

This talk is part of the CUED Computer Vision Research Seminars series.

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• CUED Computer Vision Research Seminars
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge University Engineering Department, Oatley Meeting Room (Room No. BN2-05)
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• Information Engineering Division seminar list
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