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In this paper, a novel framework for visual tracking of human body parts is introduced. The approach presented demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera by using a limb-tracking system based on a 2-D articulated model and a double-tracking strategy. Its key contribution is that the 2-D model is only constrained by biomechanical knowledge about...
A novel camera pose tracking system using a stochastic inertial-visual sensor fusion has been proposed. A method based on the Particle Filtering concept has been adapted for inertial and vision data fusion, which benefits from the agility of inertial-based tracking and robustness of vision-based camera tracking.
This paper presents an upper body tracking algorithm with a single monocular camera. In order to be suitable for human robot interaction, the designed method should be free to work on the moving camera platform and also can achieve real-time performance. The dimension of human posture model is extremely high, and we hereby focus on the visual extraction of head and arms. A hierarchical structure model...
Camera full pose estimation using only a monocular camera model is an important topic in the field of visual servoing. In this paper a simple adaptive method for updating the weights of particle filter is proposed. Using this method, the efficiency of particle filter in estimating the full pose of camera is improved. Results of the proposed method are compared with those of generic particle filter...
In order to perform autonomous manipulation in underwater missions, a robust object localization technique is crucial. This paper proposes a monocular vision based pose estimation algorithm that is intended to be a part of a supervisory manipulation system for underwater intervention missions such as DIFIS. The proposed technique incorporates a deterministic scheme into a particle filter framework...
While monocular gesture recognition slowly reaches maturity, the inclusion of 3D gestures remains a challenge. In order to enable robust and versatile depth-enabled gestures, a depth-image based tracking approach is developed. Using a model-based annealing particle filter approach, the pose of a single subject is retrieved and tracked over longer image and motion sequences. Other than many previous...
This paper presents a low cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model (HBM) is defined. Markers are detected on all camera views...
Recognition of non-verbal gestures is essential for robots to understand a user's state and intention in a Human-Robot Interaction (HRI) scenario. In this paper a multi-modal system is proposed to recognize a user's hand gestures and estimate body poses from the robot's viewpoint only. A range camera is employed to derive the depth data at a high frame rate. Depth data is useful for image segmentation,...
A global pose estimation method of an Unmanned Aerial Vehicle (UAV) by matching forward-looking aerial images from the UAV flying at low altitude with down-looking images from a satellite is proposed. To overcome the limitation of significantly different camera viewpoints and characteristics, we use buildings as a cue of matching. We extract buildings from aerial images and construct a 3D model of...
This research presents an upper body tracking method with a monocular camera. The human model is defined in a high dimensional state space. We hereby propose a hierarchical structure model to solve the tracking problem by SIR (Sampling Importance Resampling) particle filter with partitioned sampling. The image spatial and temporal information is used to track the human body and estimate the human...
This paper presents a framework for hierarchical 3D articulated human body-part tracking of two persons. We introduce a Hierarchical Annealing Particle Filter (H-APF) algorithm which uses a Hierarchical Human Body Model (HHBM) in order to perform an accurate body pose estimation of several people. The human poses are presented in a high-dimensional space. The proposed method applies a nonlinear dimensionality...
This paper presents a localization strategy for an AUV which autonomously docks on intervention panels. A brief review of past research and working solutions of docking motivates the proposed choice of the strategy. It combines a ranging sonar localization technique featuring a modified particle filter at large distance and a visual model-based pose estimation using on-board camera at close distance...
The use of a particle filter (PF) for camera pose estimation is an ongoing topic in the robotics and computer vision community, especially since the FastSLAM algorithm has been utilised for simultaneous localisation and mapping (SLAM) applications with a single camera. The major problem in this context consists in the poor proposal distribution of the camera pose particles obtained from the weak motion...
This paper presents a real-time circular targets tracking approach for camera pose estimation which is based on particle filtering framework. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model. Their ability to deal with non-linearities and non-Gaussian statistics...
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