319 research outputs found
Distributed scene reconstruction from multiple mobile platforms
Recent research on mobile robotics has produced new designs that provide
house-hold robots with omnidirectional motion. The image sensor embedded
in these devices motivates the application of 3D vision techniques on them
for navigation and mapping purposes. In addition to this, distributed cheapsensing
systems acting as unitary entity have recently been discovered as an
efficient alternative to expensive mobile equipment.
In this work we present an implementation of a visual reconstruction method,
structure from motion (SfM), on a low-budget, omnidirectional mobile platform,
and extend this method to distributed 3D scene reconstruction with
several instances of such a platform.
Our approach overcomes the challenges yielded by the plaform. The unprecedented
levels of noise produced by the image compression typical of
the platform is processed by our feature filtering methods, which ensure
suitable feature matching populations for epipolar geometry estimation by
means of a strict quality-based feature selection. The robust pose estimation
algorithms implemented, along with a novel feature tracking system,
enable our incremental SfM approach to novelly deal with ill-conditioned
inter-image configurations provoked by the omnidirectional motion. The
feature tracking system developed efficiently manages the feature scarcity
produced by noise and outputs quality feature tracks, which allow robust
3D mapping of a given scene even if - due to noise - their length is shorter
than what it is usually assumed for performing stable 3D reconstructions.
The distributed reconstruction from multiple instances of SfM is attained
by applying loop-closing techniques. Our multiple reconstruction system
merges individual 3D structures and resolves the global scale problem with
minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping
stretches of sequences. The performance of this system is demonstrated
in the 2-session case.
The management of noise, the stability against ill-configurations and the
robustness of our SfM system is validated on a number of experiments and
compared with state-of-the-art approaches. Possible future research areas
are also discussed
Intelligent computer vision processing techniques for fall detection in enclosed environments
Detecting unusual movement (falls) for elderly people in enclosed environments is receiving increasing attention and is likely to have massive potential social and economic impact.
In this thesis, new intelligent computer vision processing based techniques are proposed to detect falls in indoor environments for senior citizens living independently, such as in intelligent homes.
Different types of features extracted from video-camera recordings are exploited together with both background subtraction analysis and machine learning techniques.
Initially, an improved background subtraction method is used to extract the region of a person in the recording of a room environment. A selective updating technique is introduced for adapting the change of the background model to ensure that the human body region will not be absorbed into the background model when it is static for prolonged periods of time.
Since two-dimensional features can generate false alarms and are not invariant to different directions, more robust three-dimensional features are next extracted from a three-dimensional person representation formed from video-camera measurements of multiple calibrated video-cameras. The extracted three-dimensional features are applied to construct a single Gaussian model using the maximum likelihood technique. This can be used to distinguish falls from non-fall activity by comparing the model output with a single.
In the final works, new fall detection schemes which use only one uncalibrated video-camera are tested in a real elderly person s home environment. These approaches are based on two-dimensional features which describe different human body posture. The extracted features are applied to construct a supervised method for posture classification for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build a robust fall detection model
Bayesian framework for multiple acoustic source tracking
Acoustic source (speaker) tracking in the room environment plays an important role in many
speech and audio applications such as multimedia, hearing aids and hands-free speech communication
and teleconferencing systems; the position information can be fed into a higher
processing stage for high-quality speech acquisition, enhancement of a specific speech signal
in the presence of other competing talkers, or keeping a camera focused on the speaker in
a video-conferencing scenario. Most of existing systems focus on the single source tracking
problem, which assumes one and only one source is active all the time, and the state to be estimated
is simply the source position. However, in practical scenarios, multiple speakers may
be simultaneously active, and the tracking algorithm should be able to localise each individual
source and estimate the number of sources. This thesis contains three contributions towards
solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment.
The first contribution of this thesis is proposing a time-delay of arrival (TDOA) estimation
approach for multiple sources. Although the phase transform (PHAT) weighted generalised
cross-correlation (GCC) method has been employed to extract the TDOAs of multiple sources,
it is primarily used for a single source scenario and its performance for multiple TDOA estimation
has not been comprehensively studied. The proposed approach combines the degenerate
unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed
window-disjoint orthogonal (WDO) in the time-frequency domain, the spectrograms can
be separated by employing DUET, and the GCC method can then be applied to the spectrogram
of each individual source. The probabilities of detection and false alarm are also proposed to
evaluate the TDOA estimation performance under a series of experimental parameters.
Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman
particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem,
namely “nonconcurrent multiple acoustic tracking (NMAT)”. The extended Kalman filter
(EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF)
naturally takes the previous position estimates as well as the current TDOA measurements into
account. The proposed approach is thus able to lock on the sharp change of the source position
quickly, and avoid the tracking-lag in the general sequential importance resampling (SIR) PF.
Finally, these investigations are extended into an approach to track the multiple unknown and
time-varying number of acoustic sources. The DUET-GCC method is used to obtain the TDOA
measurements for multiple sources and a random finite set (RFS) based Rao-blackwellised PF
is employed and modified to track the sources. Each particle has a RFS form encapsulating
the states of all sources and is capable of addressing source dynamics: source survival, new
source appearance and source deactivation. A data association variable is defined to depict the
source dynamic and its relation to the measurements. The Rao-blackwellisation step is used
to decompose the state: the source positions are marginalised by using an EKF, and only the
data association variable needs to be handled by a PF. The performances of all the proposed
approaches are extensively studied under different noisy and reverberant environments, and are
favorably comparable with the existing tracking techniques
Background Subtraction in Video Surveillance
The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results
Application of computer vision for roller operation management
Compaction is the last and possibly the most important phase in construction of asphalt concrete (AC) pavements. Compaction densifies the loose (AC) mat, producing a stable surface with low permeability. The process strongly affects the AC performance properties. Too much compaction may cause aggregate degradation and low air void content facilitating bleeding and rutting. On the other hand too little compaction may result in higher air void content facilitating oxidation and water permeability issues, rutting due to further densification by traffic and reduced fatigue life. Therefore, compaction is a critical issue in AC pavement construction.;The common practice for compacting a mat is to establish a roller pattern that determines the number of passes and coverages needed to achieve the desired density. Once the pattern is established, the roller\u27s operator must maintain the roller pattern uniformly over the entire mat.;Despite the importance of uniform compaction to achieve the expected durability and performance of AC pavements, having the roller operator as the only mean to manage the operation can involve human errors.;With the advancement of technology in recent years, the concept of intelligent compaction (IC) was developed to assist the roller operators and improve the construction quality. Commercial IC packages for construction rollers are available from different manufacturers. They can provide precise mapping of a roller\u27s location and provide the roller operator with feedback during the compaction process.;Although, the IC packages are able to track the roller passes with impressive results, there are also major hindrances. The high cost of acquisition and potential negative impact on productivity has inhibited implementation of IC.;This study applied computer vision technology to build a versatile and affordable system to count and map roller passes. An infrared camera is mounted on top of the roller to capture the operator view. Then, in a near real-time process, image features were extracted and tracked to estimate the incremental rotation and translation of the roller. Image featured are categorized into near and distant features based on the user defined horizon. The optical flow is estimated for near features located in the region below the horizon. The change in roller\u27s heading is constantly estimated from the distant features located in the sky region. Using the roller\u27s rotation angle, the incremental translation between two frames will be calculated from the optical flow. The roller\u27s incremental rotation and translation will put together to develop a tracking map.;During system development, it was noted that in environments with thermal uniformity, the background of the IR images exhibit less featured as compared to images captured with optical cameras which are insensitive to temperature. This issue is more significant overnight, since nature elements are not able to reflect the heat energy from sun. Therefore to improve roller\u27s heading estimation where less features are available in the sky region a unique methodology that allows heading detection based on the asphalt mat edges was developed for this research. The heading measurements based on the slope of the asphalt hot edges will be added to the pool of the headings measured from sky region. The median of all heading measurements will be used as the incremental roller\u27s rotation for the tracking analysis.;The record of tracking data is used for QC/QA purposes and verifying the proper implementation of the roller pattern throughout a job constructed under the roller pass specifications.;The system developed during this research was successful in mapping roller location for few projects tested. However the system should be independently validated
Texture and Colour in Image Analysis
Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews
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