70 research outputs found
Camera tamper detection using wavelet analysis for video surveillance
It is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or defocusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these. © 2007 IEEE
Designing Robust Models for Behaviour Prediction using Sparse Data from Mobile Sensing: A Case Study of Office Workers’ Availability for Wellbeing Interventions
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing
reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation
of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core
challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and
2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of
deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020
challenges are designed to address research questions in these remits. In this paper, we present a summary of methods
developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by
the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and
segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled
for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also
evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The
best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences
by exploring data augmentation, data fusion, and optimal class thresholding techniques
Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network
This study reports a statistical analysis of monthly sunspot number time
series and observes non homogeneity and asymmetry within it. Using Mann-Kendall
test a linear trend is revealed. After identifying stationarity within the time
series we generate autoregressive AR(p) and autoregressive moving average
(ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of
p and q respectively. In the next phase, autoregressive neural network
(AR-NN(3)) is generated by training a generalized feedforward neural network
(GFNN). Assessing the model performances by means of Willmott's index of second
order and coefficient of determination, the performance of AR-NN(3) is
identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure
Measurement of the Nucleus Charged-Current Double-Differential Cross Section at 2.4 GeV using NOvA
The inclusive electron neutrino charged-current cross section is measured in
the NOvA near detector using protons-on-target (POT) in the
NuMI beam. The sample of GeV electron neutrino interactions is the largest
analyzed to date and is limited by 17\% systematic rather than the
7.4\% statistical uncertainties. The double-differential cross section
in final-state electron energy and angle is presented for the first time,
together with the single-differential dependence on (squared
four-momentum transfer) and energy, in the range 1 GeV 6 GeV.
Detailed comparisons are made to the predictions of the GENIE, GiBUU, NEUT, and
NuWro neutrino event generators. The data do not strongly favor a model over
the others consistently across all three cross sections measured, though some
models have especially good or poor agreement in the single differential cross
section vs.
Inflammatory bowel disease (IBD)-like disease in a case of a 33-year old man with glycogenosis 1b
The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China
Camera sabotage discovery for video surveillance applications [Video gözetleme uygulamalarinda kamera sabotaj sezimi]
In the recent years, number of surveillance cameras deployed has increased significantly. However it is important that these cameras are functioning as intended and capturing meaningful data. Offenders resort to techniques such as blocking the camera view, using a foreign object to cover the lens, spray painting or de-focusing the camera lens to prevent capture of their images and recording of their actions. These cameras are often unattended or security guard might have lost his focus and if discovery of such an attempt is not immediate, even though there is no failure in the system and the images are still being recorded, these recordings are not useful. In this paper, we propose methods for real-time discovery of visibility loss and covered camera lens using background subtraction in wavelet domain. We also propose some methods to increase robustness of the system in real-life scenarios
A multimodal approach for individual tracking of people and their belongings
In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object's size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem
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