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detection, Localization and Volume Estimation of Deflagrations

IFAC-PapersOnLine 2020年第2期53卷 8716-8723页

作者： Jakob Krooß Felix Kümmerlen Prof. Alexander Fay Department of Automation Technology University of the Federal Armed Forces Hamburg Germany Bundeswehr Research Institute for Protective Technologies and NBC Protection (WIS) Munster Germany

Deflagration-like combustions pose a serious threat in various (e.g. industrial) scenarios. Detecting, locating and distinguishing these in their developing phase may significantly reduce the resulting damage to material and individuals. In this work, an image processing based algorithm for detection, localization and volume estimation of deflagrations with a multi-camera-system is proposed. The proposed algorithm has been tested with image sequences of real deflagrations as well as possible false alarm scenarios. In comparison to state of the art methods, false alarm safety, localization quality and robustness to noise have been improved significantly.

关键词： alarm systems classification deflagrations detection algorithms fire protection image processing image segmentation multiple view geometry

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Precise Temporal Localization for Complete Actions with Quantified Temporal Structure

Precise Temporal Localization for Complete Actions with Quan...

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International Conference on Pattern Recognition

作者： Chongkai Lu Ruimin Li Hong Fu Bin Fu Yihao Wang Wai-Lun Lo Zheru Chi The Hong Kong Polytechnic University Hong Kong China Xi'an Institute of Optics and Precision Mechanics of CAS Xi'an China The Education University of Hong Kong Hong Kong China Chu Hai College of Higher Education Hong Kong China

Existing temporal action detection algorithms cannot distinguish complete and incomplete actions while this property is essential in many applications. To tackle this challenge, we proposed the action progression networks (APN), a novel model that predicts action progression of video frames with continuous numbers. Using the progression sequence of test video, on the top of the APN, a complete action searching algorithm (CAS) was designed to detect complete actions only. With the usage of frame-level fine-grained temporal structure modeling and detecting actions according to their whole temporal context, our framework can locate actions precisely and is good at avoiding incomplete action detection. We evaluated our framework on a new dataset (DFMAD-70) collected by ourselves which contains both complete and incomplete actions. Our framework got good temporal localization results with 95.77% average precision when the IoU threshold is 0.5. On the benchmark THUMOS14, an incomplete-ignostic dataset, our framework still obtain competitive performance. The code is available online at https://***/MakeCent/Action-Progression-Network.

关键词： Location awareness Estimation Benchmark testing Predictive models Prediction algorithms Pattern recognition detection algorithms

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RLS-Based detection for Massive Spatial Modulation MIMO

RLS-Based Detection for Massive Spatial Modulation MIMO

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IEEE International Symposium on Information Theory (ISIT)

作者： Bereyhi, Ali Asaad, Saba Gade, Bernhard Mueller, Ralf R. Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun Erlangen Germany

ISBN: (纸本)9781538692912

Most detection algorithms in spatial modulation (SM) are formulated as linear regression via the regularized least-squares (RLS) method. In this method, the transmit signal is estimated by minimizing the residual sum of squares penalized with some regularization. This paper studies the asymptotic performance of a generic RLS-based detection algorithm employed for recovery of SM signals. We derive analytically the asymptotic average mean squared error and the error rate for the class of bi-unitarily invariant channel matrices. The analytic results are employed to study the performance of SM detection via the box-LASSO. The analysis demonstrates that the performance characterization for i.i.d. Gaussian channel matrices is valid for matrices with non-Gaussian entries, as well. This justifies the partially approved conjecture given in [1]. The derivations further extend the former studies to scenarios with non-i.i.d. channel matrices. Numerical investigations validate the analysis, even for practical system dimensions.

关键词： Transmitting antennas Modulation Distortion Measurement Error analysis Estimation detection algorithms

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A New Robust Pupil detection Algorithm for Eye Tracking Based Human-Computer Interface

A New Robust Pupil Detection Algorithm for Eye Tracking Base...

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14th International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Bonteanu, Petronela Cracan, Arcadie Bozomitu, Radu Gabriel Bonteanu, Gabriel Gheorghe Asachi Tech Univ Iasi Fac Elect Telecommun & Informat Technol Iasi Romania

ISBN: (纸本)9781728138961

In this paper a new pupil detection algorithm appropriate for development of head mounted eye tracking interfaces is presented. The robustness of the proposed algorithm is given by its low running time and the ability to accurately provide the coordinates of the pupil center even under real-world scenarios like variable illumination and high noise conditions. The proposed algorithm is based on an adaptive quantitative binarization technique with a feedback-loop controlled threshold. The pupil center is detected by applying the Least Square Fit to Ellipse procedure to the processed eye image. The performance of this algorithm is given by a 5 pixels detection rate of 93%, obtained for the eye images from two representative databases.

关键词： Pupils Databases Gaze tracking Real-time systems Laplace equations detection algorithms Personal digital assistants

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detection of Abnormal Iris Authentication

Detection of Abnormal Iris Authentication

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Lee, Jaehoon Lee, Sang Hwa Park, Jong-Il Hanyang Univ Seoul South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781538679104

This paper deals with detecting abnormal situations in iris authentication, such as cosmetic contact lenses, printed iris images, and fake eyeballs. The proposed algorithms exploit liveness of iris images and texture features to discriminate the fake iris inputs. In the proposed process, the liveness of iris images is first evaluated by inspecting the variation of eyelid curvature, blinking, and pupil gaze. The liveness test filters the most of fake eyeball and printed iris images. Then, the features of true iris images are examined to detect cosmetic contact lenses and fake iris images which have passed the liveness test. According to the experiments with commercial mobile devices, the proposed algorithms detect abnormal iris inputs with 95% accuracy. The proposed system prevents fake and inappropriate iris inputs, thus it makes iris authentication systems to be more reliable.

关键词： Iris recognition Iris Lenses Authentication detection algorithms Eyelids Lighting

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A Two-Stage Tremor detection Algorithm for Wearable Inertial Sensors During Normal Daily Activities 41

A Two-Stage Tremor Detection Algorithm for Wearable Inertial...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： McNames, James Shah, Vrutangkumar V. Mancini, Martina Curtze, Carolin El-Gohary, Mahmoud Aboy, Mateo Carlson-Kuhta, Patricia Nutt, John G. Horak, Fay Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA APDM Inc Portland OR 97201 USA Oregon Hlth & Sci Univ Dept Neurol Portland OR 97201 USA Univ Nebraska Dept Biomech Omaha NE 68182 USA

ISBN: (纸本)9781538613115

Continuous monitoring of tremor with wearable wrist sensors during normal daily activities is more difficult than in a clinical setting when subjects perform prescribed activities because some normal daily activities resemble tremor, many normal movements contain frequency content that overlaps with the tremor frequency, and the tremor amplitude has a large dynamic range during normal daily activities. We describe a novel two-stage algorithm that offers improvement at discriminating tremor from other activities. Some of this improvement is attained by using prior domain knowledge that tremor occurs over a narrow range of frequencies for an individual, but the mean tremor frequency may vary significantly between individuals in a study population. We validated the algorithm in continuous recordings from people with Parkinson's disease and matched control subjects. The algorithm has good face validity, a low rate of false positives on recordings from control subjects (< 1.1%), and good correspondence with the constancy of rest tremor as measured by this question on the MDS-UPDRS (rho = 0.54).

关键词： Parkinson's disease Face Instruments detection algorithms Wearable sensors

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Breathing detection from tracheal sounds in both temporal and frequency domains in the context of phrenic nerve stimulation 41

Breathing detection from tracheal sounds in both temporal an...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Lu, Xinyue Guiraud, David Renaux, Serge Similowski, Thomas Azevedo, Christine Univ Montpellier INRIA Montpellier France NeuroResp Les Aires France Hosp Grp Pitie Salpetriere Charles Foix Paris France

ISBN: (纸本)9781538613115

Electrical stimulation of the phrenic nerves via implanted devices allows to counteract some disadvantages of mechanical ventilation in patients with high tetraplegia or Ondine's syndrome. Existing devices do not allow to monitor breathing or to adapt the electroventilation to patients' actual needs. A reliable breathing monitor with an inbuilt alarm function would improve patient safety. In our study, a real-time acoustic breathing detection method is proposed as a possible solution to improve implanted phrenic stimulation. A new algorithm to process tracheal sounds has been developed. It combines breathing detection in both temporal and frequency domains The algorithm has been applied on recordings from 18 healthy participants. The obtained average sensitivity, specificity and accuracy of the detection are: 99.31 %, 96.84% and 98.02%, respectively. These preliminary results show a first positive proof of the interest of such an approach. Additional experiments are needed, including longer recordings from individuals with tetraplegia or Ondine Syndrome in various environments to go further in the validation.

关键词： Frequency-domain analysis Sensitivity Monitoring Ventilation Noise measurement Sleep apnea detection algorithms

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Far Point Algorithm Active Semi-supervised Clustering for Rare Category detection 3

Far Point Algorithm Active Semi-supervised Clustering for Ra...

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3rd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Loveland, Rohan Amdahl, Jonathan Whitman Coll Dept Comp Sci Walla WA 99362 USA New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88003 USA

ISBN: (纸本)9781450376259

In some data sets the number of categories (i.e. classes) that are represented is not known in advance. The process of discovering these categories can be difficult, particularly when a data set is skewed, such that the number of data points of some classes may greatly exceed those of other classes. Rare category detection algorithms address this problem by trying to present a user with at least one data point from each category, while minimizing the overall number of data points presented. We present an algorithm based on active and semi-supervised learning that finds category clusters using a query selection strategy that maximizes the distance from a set of already labeled data points to a query data point. We evaluate the algorithm's performance on artificially skewed versions of the MNIST data set as a rare category detection algorithm, investigating differences in performance due to both the effects of relative frequency and inherent class structure differences in feature space.

关键词： detection algorithms machine learning algorithms partitioning algorithms

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Smartwatch PPG Peak detection Method for Sinus Rhythm and Cardiac Arrhythmia 41

Smartwatch PPG Peak Detection Method for Sinus Rhythm and Ca...

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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Han, Dong Bashar, Syed Khairul Lazaro, Jesus Ding, Eric Whitcomb, Cody McManus, David D. Chon, Ki H. Univ Connecticut Storrs CT 06269 USA Univ Zaragoza Inst Invest Sanitaria IIS Aragon Inst Engn Res I3A Commun Technol Grp Maria de Luna 1 Zaragoza 50015 Spain CIBER Bioengn Biomat Nanomed CIBER BBN Zaragoza 50018 Spain Univ Massachusetts Med Sch Div Cardiol Worcester MA 01655 USA

ISBN: (纸本)9781538613115

The aim of our work herein was to design a photoplethysmographic (PPG) peak detection algorithm which automatically detect and discriminate various cardiac rhythms-normal sinus rhythms (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF)-for PPG signals collected on smartwatch. Compared with peak detection algorithm designed for NSR, the novelty is that our proposed peak detection algorithm can accurately estimate heart rates (HR) among various arrhythmias, which enhances the accuracy of AF screening. Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate various arrhythmias, as described above. Moreover, a novel Poincare plot scheme is used to discriminate AF with Rapid Ventricular Response (RVR) from normal basal heart rate AF. Moreover, the method is also able to differentiate PAC/PVC from NSR and AF. Our results show that the proposed peak detection algorithm provides significantly lower average beat-to-beat estimation error (> 40% lower) and mean heart rate estimation error (> 50% lower) when compared to a traditional peak detection algorithm that is known to be accurate for NSR. Our new approach allows more accurate HR estimation as it can account for various arrhythmias which previous PPG peak detection algorithms were designed solely for NSR.

关键词： detection algorithms Electrocardiography Rhythm Heart beat Picture archiving and communication systems Motion artifacts

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UHCTD: A Comprehensive Dataset for Camera Tampering detection 16

UHCTD: A Comprehensive Dataset for Camera Tampering Detectio...

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16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Mantini, Pranav Shah, Shishir K. Univ Houston Dept Comp Sci Houston TX 77004 USA

ISBN: (纸本)9781728109909

An unauthorized or an accidental change in the view of a surveillance camera is called a tampering. algorithms that detect tampering by analyzing the video are referred to as camera tampering detection algorithms. Most evaluations on camera tampering detection methods are presented based on individually collected datasets. One of the major challenges in the area of camera tamper detection is the absence of a public dataset with sufficient size and variations for an extensive performance evaluation. We propose a large scale synthetic dataset called University of Houston Camera Tampering detection dataset (UHCTD) for development and testing of camera tampering detection methods. The dataset consists of a total 576 tampers with over 288 hours of video captured from two surveillance cameras. To establish an initial benchmark, we cast camera tampering detection as a classification problem. We train and evaluate three different deep architectures that have shown promise in scene classification, Alexnet, Resnet, and Densenet. Results are presented to show how the dataset can be used to train and classify images as normal, and tampered within and across cameras.

关键词： Cameras Surveillance detection algorithms Lenses Meteorology Testing Lighting

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