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We present a method for full-motion recovery of the head pose from a monocular video input based on an accurate head model and textures. We build our face model using a distribution of high resolution 3D face scans. The cost of computations makes us select parts of this full model. To address the difficult task of initializing the model position and tracking its motion, we use a composite metric using...
This work is motivated by the limitations of current techniques to visualize the heart as it moves under contraction and respiration during interventional procedures such as ablation of atrial fibrillation. Our long-term goal is to integrate high resolution models routinely obtained from pre-procedure imaging (here, via MRI) with the low resolution, sparse, images, along with a few scalar measurements...
Object tracking in video sequences has been extensively studied in computer vision. Although promising results have been achieved, often the proposed solutions are tailored for particular objects, structured to specific conditions or constrained by tight guidelines. In real cases it is difficult to recognize these situations automatically because a large number of parameters must be tuned. Factors...
This paper presents a novel framework for activities perception in video surveillance scenarios. Firstly, moving objects are detected by modeling the background using Gaussian Mixture Model (GMM). Secondly, a novel adaptive particle filter (APF) is introduced. The proposed APF has time-varying dimensions and can track multiple moving objects entering or leaving the field of view effectively. Finally,...
The particle filter is a popular tool for visual tracking. Traditionally, the number of particles used is typically fixed, and the motion model is simply a random walk with fixed noise variance. All these factors make the visual tracker unstable. To stabilize the tracker and guarantee the real-time tracking, an adaptive particle filter algorithm which estimates the motion model parameters using principal...
Hand tracking is an active research topic in Human Computer Interaction (HCI). In this paper, we present an improved Unscented Particle Filter (UPF) combined with the incremental Principle Component Analysis (IPCA) method for the visual hand tracking. The Singular Value Decomposition (SVD) approach is introduced to compute the sigma points and then to obtain the proposal distribution within the Unscented...
We propose a novel object tracking algorithm based on modeling the target appearance in a joint space. In contrast with traditional histogram-based trackers which discard all spatial information, the joint space takes both the photometric and spatial information into account. Within this joint space, the target is modeled in a Gaussian mixtures manner where a richer description of the target is captured...
We proposed a novel approach to track groups of people observed by a static camera. The approach relies on a unified Bayes' theorem based framework to model both scene background and the color distribution of targets. By sharing the framework, it is more simple and low cost to construct a practical surveillance system. Additionally, the framework has the advantages of insensitiveness to initial observations...
This paper presents a novel approach to model the complex motion of human using a probabilistic autoregressive moving average model. The parameters of the model are adaptively tuned during the course of tracking by utilizing the main varying components of the pdf of the target's acceleration and velocity. This motion model, along with the color histogram as the measurement model, has been incorporated...
A novel strategy of simultaneously tracking and segmentation is proposed for human respiratory rate estimation from thermal infrared, which can be applicable to contact-free polygraphy, airport health screening and patient monitoring system. In this framework, by carefully selecting the adaptive observation model for the tracking template and taking the intensity variation pattern of breathing into...
Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues...
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
This paper proposes a novel method for rapid and robust human detection and tracking based on the omega-shape features of people's head-shoulder parts. There are two modules in this method. In the first module, a Viola-Jones type classifier and a local HOG (Histograms of Oriented Gradients) feature based AdaBoost classifier are combined to detect head-shoulders rapidly and effectively. Then, in the...
Although primates can facilely maintain long-duration tracking of an object without infection of occlusion or other near similar distracters, it remains a challenge for computer vision system. Studies in psychology suggest that the ability of primates to focus selective attention on the spatial properties of an object is necessary to observe object quickly and efficiently while focus selective attention...
This paper presents an Intelligent Surveillance System which can be used in real scenes. Our aim is to establish a reliable background from a complex one via a accurate segmentation method followed by object tracking. The system is able to continue tracking the objects regardless if the objects are overlapping or separate. The system uses the TI TMS320DM6446 Davinci development kit, inputs image sequence...
We present a markerless tracking system for unconstrained human motions which are typical for everyday manipulation tasks. Our system is capable of tracking a high-dimensional human model (51 DOF) without constricting the type of motion and the need for training sequences. The system reliably tracks humans that frequently interact with the environment, that manipulate objects, and that can be partially...
One of the key issues related to object tracking is the representation of the object motion. It is a challenging problem because the object usually exhibits complex and rich dynamic behavior. In this paper, we propose an adaptive dynamic model to describe the dynamics/motion of the object and embed it into the particle filter framework for visual object tracking. The model characterize the object...
This paper mainly studies angle-measurement based multi-model filtering algorithm to overcome the existing problems in the applications of traditional filtering approaches for bearing-only target locating and tracking system. By analyzing bearing-only target motion characteristic and the feasibility of using multi-model technology, the IMM is adopt as filtering algorithm to solve existing problem...
This paper presents a method that can track non-rigid moving objects using adaptive particle filter based on spatiograms. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in target tracking. Two key components of particle filters are observation models and motion models. Firstly, because the observation model based on general color histograms...
This paper presents a multi-view approach to the tracking of people location and orientation. To achieve efficient and accurate likelihood evaluation, a novel likelihood computation method is proposed. Mixtures of Gaussian (MoG) are used to represent the color models of subjects. The scaled unscented transformation is used to project the MoG color models onto the image plane to predict the color distribution...
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