1,265 research outputs found
Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series
Novelty detection is a process for distinguishing the observations that differ in some respect
from the observations that the model is trained on. Novelty detection is one of the fundamental
requirements of a good classification or identification system since sometimes the
test data contains observations that were not known at the training time. In other words, the
novelty class is often is not presented during the training phase or not well defined.
In light of the above, one-class classifiers and generative methods can efficiently model
such problems. However, due to the unavailability of data from the novelty class, training
an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in
unsupervised and semi-supervised settings is a crucial step in such tasks.
In this thesis, we propose several methods to model the novelty detection problem in
unsupervised and semi-supervised fashion. The proposed frameworks applied to different
related applications of anomaly and outlier detection tasks. The results show the superior of
our proposed methods in compare to the baselines and state-of-the-art methods
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
Real-time visual perception : detection and localisation of static and moving objects from a moving stereo rig
International audienceWe present a novel method for scene reconstruction and moving object detection and tracking, using extensive point tracking (typically more than 4000 points per frame) over time. Current neighbourhood is reconstructed in the form of a 3D point cloud, which allows for extra features (ground detection, path planning, obstacle detection). Reconstruction framework takes moving objects into account, and tracking over time allows for trajectory and speed estimation
Gaussian mixture model classifiers for detection and tracking in UAV video streams.
Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces.
This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter.
The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers
Pilot/vehicle model analysis of visual and motion cue requirements in flight simulation
The optimal control model (OCM) of the human operator is used to predict the effect of simulator characteristics on pilot performance and workload. The piloting task studied is helicopter hover. Among the simulator characteristics considered were (computer generated) visual display resolution, field of view and time delay
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