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It is difficult to address the problem of occlusion and loss of target when a single image tracking algorithm is utilized in high reliability occasions, such as prison, coal mines and military area. This paper presents a novel tracking and monitoring system based on wireless location and video. A coordinate frame of wireless system of scene constructed by binding the targets with RSSI labels is used...
Percutaneous needle procedures are mostly carried out with the guidance of 2D ultrasound (US) imaging. US images are inherently noisy and their resolutions are low. Hence, target tracking can be challenging. Image based tracking methods can be used to track the needle and the target. This paper proposes visual tracking of multiple moving points, such as biopsy needles and targets, in 2D US images...
Object representation is a major component in object tracking, however, most conventional patch-based methods just simply decompose the object into patches with grid or stochastic rectangles. This kind of decomposition ignores the intrinsic structure of object, leading to low discriminative power and weak representation effectiveness when similar objects appear or under background clutters. In this...
Acquiring the accurate 3-D position of a target person around a robot provides fundamental and valuable information that is applicable to a wide range of robotic tasks, including home service, navigation and entertainment. This paper presents a real-time robotic 3-D human tracking system which combines a monocular camera with an ultrasonic sensor by the extended Kalman filter (EKF). The proposed system...
The change of appearance of the target object is one of important issue in visual tracking. It is because some factors such as camera motion, illumination change, motion change, occlusion, and size change are influenced to the object target during tracking. Recently, discriminative correlation filters (DCF) gave good results to handle these problems. Unfortunately, the DCF only works in the single-resolution...
This paper addresses vision-based tracking and landing of a micro-aerial vehicle (MAV) on a ground vehicle (GV). The camera onboard the MAV is mounted so that the optical axis is aligned with the downward-facing axis of the body-fixed frame. A novel supervised learning vision algorithm is proposed as the method to detect the ground vehicle in the image frame. A feedback linearization technique is...
In this paper, we propose an algorithm for tracking of moving objects in video sequences. Our method uses Kalman filter to predict the location of target and exploits superpixel based tracking algorithm to find the real position of target in a search region surrounding the predicted location. The motion dynamics and equations from mechanics physics are used to design a Kalman filter with assumption...
This paper designs and implements a following robot system based on practical monocular vision sensor. The system can autonomously follow a specific target human body as needed. In the initialization phase, the system uses the background difference method to obtain the motion region and extract the target. In the second phase, continuously adaptive mean-shift algorithm (Camshift) is used to the target...
Traditional kernelized correlation filter tracking methods use the target position in the current frame to estimate the moving target initial position in the next frame. For fast moving target, these methods lose the target easily. To cope with this problem, a novel scale-adaptive regression position prediction tracking approach is proposed. This algorithm employs regression prediction method to predict...
In this paper, we exploit deep convolutional features for object appearance modeling and propose a simple while effective deep discriminative model (DDM) for visual tracking. The proposed DDM takes as input the deep features and outputs an object-background confidence map. Considering that both spatial information from lower convolutional layers and semantic information from higher layers benefit...
Correlation filter-based tracking methods have accomplished competitive performance on accuracy and robustness, but there is still a huge potential in choosing suitable features. Recently, Convolutional Kernel Networks (CKN), which provide a fast and simple procedure to approximate kernel descriptors, have been proposed and achieved state-of-the-art performance in many vision tasks. In this paper,...
Recently, CNN (Convolutional Neural Network) based trackers have achieved promising results benefited from their robust feature representation. However, most trackers only use features from a certain layer, which limits their performance. In this paper, we propose a novel CNN based tracker. Firstly, we use local detection and global detection network for target localization. In local detection network,...
This work seeks to apply the emerging virtual and mixed reality techniques to visual exploration and visualization of earth science data. A novel system is developed to facilitate a collaborative mixed reality visualization, enabling both in-situ and off-site users to simultaneously interact with and visualize science data within mixed reality realm. We implement the prototype system in the context...
We present a system enabling users to accurately catch a real ball while immersed in a virtual reality environment. We examine three visualizations: rendering a matching virtual ball, the predicted trajectory of the ball, and a target catching point lying on the predicted trajectory. In our demonstration system, we track the projectile motion of a ball as it is being tossed between users. Using Unscented...
As interaction techniques involving scaling of motor space in virtual reality are becoming more prevalent, it is important to understand how individuals adapt to such scalings and how they re-adapt back to non-scaled norms. This preliminary work examines how individuals, performing a targeted ball throwing task, adapted to addition and removal of a translational scaling of the ball's forward flight...
Implementing robust and real-time visual tracking method is a challenging task due to many disturbing factors such as illumination changes, appearance changes, noises and irregular dynamic occlusion, etc. In order to solve this problem, a moving target tracking method, which can update the background template real-time with memory and establish self-adaptive background template library, is presented...
It is a challenging task to develop a robust appearance model due to various factors such as partial occlusion, fast motion, background clutters and illumination variations. In this paper, we propose a novel target representation for visual tracking. Namely, a target candidate is represented by sparse affine combinations of dictionary templates in a particle filter framework. Affine combinations based...
Visual object tracking is a fundamental task in many high-level computer vision applications. Most existing algorithms have to build complex models with expensive computation to achieve accurate object tracking, which brings significant difficulty in real-time tracking. In order to address this problem, motivated by recent success of high-speed correlation filter (CF) models, a novel real-time object...
Sparse representation has been exploited for visual object tracking to yield impressive performance in recent years. However, sparse models in tracking are commonly limited by two intrinsic challenges, i.e., the high computational cost that makes it impractical in real-world applications and few reliable target observations available in dictionary initialization that renders it hard to handle great...
This paper proposes a patch-based keypoints clustering method for long term robust visual tracking. We plan to employ a parallel framework with keypoints matching and estimation for tracking purpose. Patch-based method is implemented in our algorithm to improve the flexibility of system. The template is divided into patches to ensure the spatial constraint of local keypoints. The motion cue of patches...
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