3,444 research outputs found
Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking
Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets. 1
Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing
neighboring organs at risk (OARs). Increasingly complex treatment techniques
such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery
(SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been
developed to deliver doses more precisely to the target. While such
technologies have improved dose delivery, the implementation of intra-fraction
motion management to verify tumor position at the time of treatment has become
increasingly relevant. Recently, artificial intelligence (AI) has demonstrated
great potential for real-time tracking of tumors during treatment. However,
AI-based motion management faces several challenges including bias in training
data, poor transparency, difficult data collection, complex workflows and
quality assurance, and limited sample sizes. This review serves to present the
AI algorithms used for chest, abdomen, and pelvic tumor motion
management/tracking for radiotherapy and provide a literature summary on the
topic. We will also discuss the limitations of these algorithms and propose
potential improvements.Comment: 36 pages, 5 Figures, 4 Table
4D Scene Reconstruction in Multi-Target Scenarios
In this report, we introduce a complex approach on 4D reconstruction of dynamic scenarios containing multiple walking pedestrians. The input of the process is a point cloud sequence recorded by a rotating multi-beam Lidar sensor, which monitors the scene from a fixed position. The output is a geometrically reconstructed and textured scene containing moving 4D people models, which can follow in real time the trajectories of the walking pedestrians observed on the Lidar data flow. Our implemented system consists of four main steps. First, we separate foreground and background regions in each point cloud frame of the sequence by a robust probabilistic approach. Second, we perform moving pedestrian detection and tracking, so that among the point cloud regions classified as foreground, we separate the different objects, and assign the corresponding people positions to each other over the consecutive frames of the Lidar measurement sequence. Third, we geometrically reconstruct the ground, walls and further objects of the background scene, and texture the obtained models with photos taken from the scene. Fourth we insert into the scene textured 4D models of moving pedestrians which were preliminary created in a special 4D reconstruction studio. Finally, we integrate the system elements in a joint dynamic scene model and visualize the 4D scenario
Coronary Artery Segmentation and Motion Modelling
Conventional coronary artery bypass surgery requires invasive sternotomy and the
use of a cardiopulmonary bypass, which leads to long recovery period and has high
infectious potential. Totally endoscopic coronary artery bypass (TECAB) surgery
based on image guided robotic surgical approaches have been developed to allow the
clinicians to conduct the bypass surgery off-pump with only three pin holes incisions
in the chest cavity, through which two robotic arms and one stereo endoscopic camera
are inserted. However, the restricted field of view of the stereo endoscopic images leads
to possible vessel misidentification and coronary artery mis-localization. This results
in 20-30% conversion rates from TECAB surgery to the conventional approach.
We have constructed patient-specific 3D + time coronary artery and left ventricle
motion models from preoperative 4D Computed Tomography Angiography (CTA)
scans. Through temporally and spatially aligning this model with the intraoperative
endoscopic views of the patient's beating heart, this work assists the surgeon to identify
and locate the correct coronaries during the TECAB precedures. Thus this work has
the prospect of reducing the conversion rate from TECAB to conventional coronary
bypass procedures.
This thesis mainly focus on designing segmentation and motion tracking methods
of the coronary arteries in order to build pre-operative patient-specific motion models.
Various vessel centreline extraction and lumen segmentation algorithms are presented,
including intensity based approaches, geometric model matching method and
morphology-based method. A probabilistic atlas of the coronary arteries is formed
from a group of subjects to facilitate the vascular segmentation and registration procedures.
Non-rigid registration framework based on a free-form deformation model
and multi-level multi-channel large deformation diffeomorphic metric mapping are
proposed to track the coronary motion. The methods are applied to 4D CTA images
acquired from various groups of patients and quantitatively evaluated
Human pose and skeleton reconstruction with deep neural networks from mmWave radar point clouds
The foreseen adoption of millimeter-wave (mmWave) communication systems in our everyday life has increased the research interest on using this technology for sensing applications. Previous work has shown that the reflections of the signals transmitted by a mmWave device can be used to localize objects or people in the environment and analyze their movement. Higher carrier frequencies, with their large bandwidth availability are key to obtain better spatial resolution, effectively allowing the usage of dedicated mmWave radar devices for the fine-grained analysis of human gaits. This is of particular interest in bio-mechanical motion tracking systems used in clinical and rehabilitation contexts, which are typically based on expensive and impractical marker-based devices. In this work we design and implement a deep-learning framework for mmWave radar-based motion tracking of human gait, which can reconstruct the position of a set of key points on the human body from the raw radar signal during motion. The proposed system extracts point clouds representing the moving subject, making use of signal processing and tracking techniques to remove noise from static objects and interference. Then, we adapt a state-of-the-art Neural Network (NN) for point clouds, Pointnet++, to our sparse and noisy radar data and propose a modification of the architecture to exploit the temporal correlation of radar point clouds. The results obtained on our own dataset show promising results despite the technical limitations of the motion tracking system used as a ground truth to train the proposed NN. Our system can reconstruct the position of 30 markers placed on the subject’s body with an average accuracy of 17 cm, paving the road for future work on mmWave markerless motion tracking based on a larger and more diverse set of measurements
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