Simultaneous segmentation and pose estimation of humans using dynamic graph cuts

This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or articulated object

Computer vision based interfaces for computer games

Interacting with a computer game using only a simple web camera has seen a great deal of success in the computer games industry, as demonstrated by the numerous computer vision based games available for the Sony PlayStation 2 and PlayStation 3 game consoles. Computational efficiency is important for these human computer inter- action applications, so for simple interactions a fast background subtraction approach is used that incorporates a new local descriptor which uses a novel temporal coding scheme that is much more robust to noise than the standard formulations. Results are presented that demonstrate the effect of using this method for code label stability. Detecting local image changes is sufficient for basic interactions, but exploiting high-level information about the player's actions, such as detecting the location of the player's head, the player's body, or ideally the player's pose, could be used as a cue to provide more complex interactions. Following an object detection approach to this problem, a combined detection and segmentation approach is explored that uses a face detection algorithm to initialise simple shape priors to demonstrate that good real-time performance can be achieved for face texture segmentation. Ultimately, knowing the player's pose solves many of the problems encountered by simple local image feature based methods, but is a difficult and non-trivial problem. A detection approach is also taken to pose estimation: first as a binary class problem for human detection, and then as a multi-class problem for combined localisation and pose detection. For human detection, a novel formulation of the standard chamfer matching algo- rithm as an SVM classifier is proposed that allows shape template weights to be learnt automatically. This allows templates to be learnt directly from training data even in the presence of background and without the need to pre-process the images to extract their silhouettes. Good results are achieved when compared to a state of the art human detection classifier. For combined pose detection and localisation, a novel and scalable method of ex- ploiting the edge distribution in aligned training images is presented to select the most potentially discriminative locations for local descriptors that allows a much higher space of descriptor configurations to be utilised efficiently. Results are presented that show competitive performance when compared to other combined localisation and pose detection methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Randomized trees for human pose detection

This paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. The first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottomup approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images; in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras. 1

Nuclear modification factor of light neutral-meson spectra up to high transverse momentum in p–Pb collisions at sNN=8.16 TeV

International audienceNeutral pion (π0) and η meson production cross sections were measured up to unprecedentedly high transverse momenta (pT) in p–Pb collisions at sNN=8.16TeV. The mesons were reconstructed via their two-photon decay channel in the rapidity interval −1.3<y<0.3 in the ranges of 0.4<pT<200 GeV/c and 1.0<pT<50 GeV/c, respectively. The respective nuclear modification factor (RpPb) is presented for pT up to of 200 and 30 GeV/c, where the former was achieved by extending the π0 measurement in pp collisions at s=8TeV using the merged cluster technique. The values of RpPb are below unity for pT<10 GeV/c, while they are consistent with unity for pT>10 GeV/c, leaving essentially no room for final state energy loss. The new data provide strong constraints for nuclear parton distribution and fragmentation functions over a broad kinematic range and are compared to model predictions as well as previous results at sNN=5.02TeV