Automatic and Efficient Human Pose Estimation for Sign Language Videos

We present a fully automatic arm and hand tracker that detects joint positions over continuous sign language video sequences of more than an hour in length. To achieve this, we make contributions in four areas: (i) we show that the overlaid signer can be separated from the background TV broadcast using co-segmentation over all frames with a layered model; (ii) we show that joint positions (shoulders, elbows, wrists) can be predicted per-frame using a random forest regressor given only this segmentation and a colour model; (iii) we show that the random forest can be trained from an existing semi-automatic, but computationally expensive, tracker; and, (iv) introduce an evaluator to assess whether the predicted joint positions are correct for each frame. The method is applied to 20 signing footage videos with changing background, challenging imaging conditions, and for different signers. Our framework outperforms the state-of-the-art long term tracker by Buehler et al. (International Journal of Computer Vision 95:180–197, 2011), does not require the manual annotation of that work, and, after automatic initialisation, performs tracking in real-time. We also achieve superior joint localisation results to those obtained using the pose estimation method of Yang and Ramanan  (Proceedings of the IEEE conference on computer vision and pattern recognition, 2011).     

Efficient collision detection for models deformed by morphing

Published online: 5 February 2003     

Advanced methods for the estimation of an unknown projection from a map

This article presents three new methods (M5, M6, M7) for the estimation of an unknown map projection and its parameters. Such an analysis is beneficial and interesting for historic, old, or current maps without information about the map projection; it could improve their georeference. The location similarity approach takes into account the residuals on the corresponding features; the minimum is found using the non-linear least squares. For the shape similarity approach, the minimized objective function ? takes into account the spatial distribution of the features, together with the shapes of the meridians, parallels and other 0D-2D elements. Due to the non-convexity and discontinuity, its global minimum is determined using the global optimization, represented by the differential evolution. The constant values of projection φ _k , λ _k , φ ₁, λ ₀, and map constants R,ΔX,ΔY, α (in relation to which the methods are invariant) are estimated. All methods are compared and the results are presented for the synthetic data as well as for 8 early maps from the Map Collection of the Charles University and the David Rumsay Map Collection. The proposed algorithms have been implemented in the new version of the detectproj software.