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Long-term tracking of human pose in videos

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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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Pfizer Lecture Theatre, Department of Chemistry, Lensfield Road, University of Cambridge, CB2 1EW LT1, Cambridge Judge Business School Cambridge University Engineering Department, TBA

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