20 research outputs found
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.Comment: CVPR 201
Human Body Part Labeling and Tracking Using Graph Matching Theory
International audienceProperly labeling human body parts in video sequencesis essential for robust tracking and motion interpretationframeworks. We propose to perform this task by usingGraph Matching. The silhouette skeleton is computed anddecomposed into a set of segments corresponding to the differentlimbs. A Graph capturing the topology of the segmentsis generated and matched against a 3D model of thehuman skeleton. The limb identification is carried out foreach node of the graph, potentially leading to the absenceof correspondence. The method captures the minimal informationabout the skeleton shape. No assumption about theviewpoint, the human pose, the geometry or the appearenceof the limbs is done during the matching process, making theapproach applicable to every configuration. Some correspondancesthat might be ambiguous only relying on topologyare enforced by tracking each graph node over time.Several results present the efficiency of the labeling, particularlyits robustness to limb detection errors that are likelyto occur in real situations because of occlusions or low levelsystem failures. Finally the relevance of the labeling in anoverall tracking system is pointed out
СЕГМЕНТАЦИЯ ОБЪЕКТОВ НА БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ БИБЛИОТЕКИ ШАБЛОНОВ
The purpose of this paper is to introduce a robust framework to facilitate simultaneous detection and segmentation of objects with arbitrary size and shape on different kinds of medical images using a library of arbitrary irregular smooth shapes.Рассматривается система компьютеризированной диагностики для обнаружения объектов с произвольными размерами и формой и сегментации их на медицинских изображениях различной мо-дальности с использованием библиотеки шаблонов нерегулярной гладкой формы
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
This paper considers the task of articulated human pose estimation of
multiple people in real world images. We propose an approach that jointly
solves the tasks of detection and pose estimation: it infers the number of
persons in a scene, identifies occluded body parts, and disambiguates body
parts between people in close proximity of each other. This joint formulation
is in contrast to previous strategies, that address the problem by first
detecting people and subsequently estimating their body pose. We propose a
partitioning and labeling formulation of a set of body-part hypotheses
generated with CNN-based part detectors. Our formulation, an instance of an
integer linear program, implicitly performs non-maximum suppression on the set
of part candidates and groups them to form configurations of body parts
respecting geometric and appearance constraints. Experiments on four different
datasets demonstrate state-of-the-art results for both single person and multi
person pose estimation. Models and code available at
http://pose.mpi-inf.mpg.de.Comment: Accepted at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2016
Autonomous UAV for suspicious action detection using pictorial human pose estimation and classification
Visual autonomous systems capable of monitoring crowded areas and alerting the authorities in occurrence of a suspicious action can play a vital role in controlling crime rate. Previous atte mpts have been made to monitor crime using posture recognition but nothing exclusive to investigating actions of people in large populated area has been cited. In order resolve this shortcoming, we propose an autonomous unmanned aerial vehicle (UAV) visual surveillance system that locates humans in image frames followed by pose estimation using weak constraints on position, appearance of body parts and image parsing. The estimated pose, represented as a pictorial structure, is flagged using the proposed Hough Orientation Calculator (HOC) on close resemblance with any pose in the suspicious action dataset. The robustness of the system is demonstrated on videos recorded using a UAV with no prior knowledge of background, lighting or location and scale of the human in the image. The system produces an accuracy of 71% and can also be applied on various other video sources such as CCTV camera
Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking
3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectors’ state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results