1,767 research outputs found
On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data
In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here
Automated 3D model generation for urban environments [online]
Abstract
In this thesis, we present a fast approach to automated
generation of textured 3D city models with both high details at
ground level and complete coverage for birds-eye view.
A ground-based facade model is acquired by driving a vehicle
equipped with two 2D laser scanners and a digital camera under
normal traffic conditions on public roads. One scanner is
mounted horizontally and is used to determine the approximate
component of relative motion along the movement of the
acquisition vehicle via scan matching; the obtained relative
motion estimates are concatenated to form an initial path.
Assuming that features such as buildings are visible from both
ground-based and airborne view, this initial path is globally
corrected by Monte-Carlo Localization techniques using an aerial
photograph or a Digital Surface Model as a global map. The
second scanner is mounted vertically and is used to capture the
3D shape of the building facades. Applying a series of automated
processing steps, a texture-mapped 3D facade model is
reconstructed from the vertical laser scans and the camera
images. In order to obtain an airborne model containing the roof
and terrain shape complementary to the facade model, a Digital
Surface Model is created from airborne laser scans, then
triangulated, and finally texturemapped with aerial imagery.
Finally, the facade model and the airborne model are fused
to one single model usable for both walk- and fly-thrus. The
developed algorithms are evaluated on a large data set acquired
in downtown Berkeley, and the results are shown and discussed
RGB-D datasets using microsoft kinect or similar sensors: a survey
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
Modeling and Simulation in Engineering
This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results
Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul
Deploying deep learning-based applications in specialized domains like the
aircraft production industry typically suffers from the training data
availability problem. Only a few datasets represent non-everyday objects,
situations, and tasks. Recent advantages in research around Vision Foundation
Models (VFM) opened a new area of tasks and models with high generalization
capabilities in non-semantic and semantic predictions. As recently demonstrated
by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a
promising direction in tackling the boundaries spanned by data, context, and
sensor variety. Although, investigating its application within specific domains
is subject to ongoing research. This paper contributes here by surveying
applications of the SAM in aircraft production-specific use cases. We include
manufacturing, intralogistics, as well as maintenance, repair, and overhaul
processes, also representing a variety of other neighboring industrial domains.
Besides presenting the various use cases, we further discuss the injection of
domain knowledge
3DFusion, A real-time 3D object reconstruction pipeline based on streamed instance segmented data
This paper presents a real-time segmentation and reconstruction system that
utilizes RGB-D images to generate accurate and detailed individual 3D models of
objects within a captured scene. Leveraging state-of-the-art instance
segmentation techniques, the system performs pixel-level segmentation on RGB-D
data, effectively separating foreground objects from the background. The
segmented objects are then reconstructed into distinct 3D models in a
high-performance computation platform. The real-time 3D modelling can be
applied across various domains, including augmented/virtual reality, interior
design, urban planning, road assistance, security systems, and more. To achieve
real-time performance, the paper proposes a method that effectively samples
consecutive frames to reduce network load while ensuring reconstruction
quality. Additionally, a multi-process SLAM pipeline is adopted for parallel 3D
reconstruction, enabling efficient cutting of the clustering objects into
individuals. This system employs the industry-leading framework YOLO for
instance segmentation. To improve YOLO's performance and accuracy,
modifications were made to resolve duplicated or false detection of similar
objects, ensuring the reconstructed models align with the targets. Overall,
this work establishes a robust real-time system with a significant enhancement
for object segmentation and reconstruction in the indoor environment. It can
potentially be extended to the outdoor scenario, opening up numerous
opportunities for real-world applications
Cross-layer Optimized Wireless Video Surveillance
A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system.
The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion.
In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality.
The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos.
In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work.
Adviser: Song C
Cross-layer Optimized Wireless Video Surveillance
A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system.
The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion.
In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality.
The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos.
In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work.
Adviser: Song C
Automated inter-plant spacing sensing of corn plant seedlings and quantification of laying hen behaviors using 3D computer vision
Within-row plant spacing plays an important role in uniform distribution of water and nutrients among plants, hence affects the final crop yield. While manual in-field manual measurements of within-row plant spacing is time and labor intensive, little work has been carried out to automate the process. An automated system is developed using a state-of-the-art 3D vision sensor that accurately measures within-row corn plant spacing. The system is capable of processing about 1200 images captured from a 61 m crop row containing approximately 280 corn plants in about three and half minutes.
Stocking density of laying hens in egg production remains an area of investigation from the standpoints of ensuring hen\u27s ability to perform natural behaviors and production economic efficiency. It is therefore of socio-economic importance to quantify the effect of stocking density on laying hens behaviors and thus wellbeing. In this study, a novel method for automatic quantification of stocking density effect on some natural laying hen behaviors such as locomotion, perching, feeding, drinking and nesting is explored. Image processing techniques are employed on top view images captured with a state-of-the-art time-of-flight (TOF) of light based 3D vision camera for identification as well as tracking of individual hens housed in a 1.2 m 1.2 m pen. A Radio Frequency Identification (RFID) sensor grid consisting of 20 antennas installed underneath the pen floor is used as a recovery system in situations where the imaging system fails to maintain identities of the hens
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