scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2020 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Weather Variation ◽  
The Impact ◽  
Haze Days

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the proposed DFA approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to further reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by the presented DFA, which can improve the average classification accuracy in all seasons. At the end of the paper, it is verified by cross validation that the DFA method can be stabilized at around 93% even in hazy day and snowy day which has stability and universality in any weather conditions, even in snowy and haze days.

scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2020 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Weather Variation ◽  
The Impact ◽  
Haze Days

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the proposed DFA approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to further reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by the presented DFA, which can improve the average classification accuracy in all seasons. At the end of the paper, it is verified by cross validation that the DFA method can be stabilized at around 93% even in hazy day and snowy day which has stability and universality in any weather conditions, even in snowy and haze days.

scholarly journals Deep factor analysis for weather varied sense-through-foliage target detection

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Average Classification Accuracy ◽  
Weather Variation ◽  
The Impact

AbstractIn this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

scholarly journals Deep Factor Analysis for Weather Varied Sense-through-foliage Target Detection

2021 ◽  
Author(s):  
Wenling Xue ◽  
Ting Jiang ◽  
Xuebin Sun ◽  
Xiaokun Zheng ◽  
Xue Ding
Keyword(s):  
Factor Analysis ◽  
Target Detection ◽  
Background Noise ◽  
Cross Validation ◽  
Weather Conditions ◽  
Signal Denoising ◽  
Detection Accuracy ◽  
Average Classification Accuracy ◽  
Weather Variation ◽  
The Impact

Abstract In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis(DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in foggy and snowy days, which can be stable at about 93%.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals Supervised Distance-Based Feature Selection for Hyperspectral Target Detection

2019 ◽  
Vol 11 (17) ◽  
pp. 2049 ◽  
Author(s):  
Moeini Rad ◽  
Abkar ◽  
Mojaradi
Keyword(s):  
Target Detection ◽  
Area Under The Curve ◽  
Image Clustering ◽  
False Alarms ◽  
Detection Accuracy ◽  
Operating Characteristics ◽  
Autocorrelation Matrix ◽  
Target Energy ◽  
Roc Area ◽  
The Impact

Feature/band selection (FS/BS) for target detection (TD) attempts to select features/bands that increase the discrimination between the target and the image background. Moreover, TD usually suffers from background interference. Therefore, bands that help detectors to effectively suppress the background and magnify the target signal are considered to be more useful. In this regard, three supervised distance-based filter FS methods are proposed in this paper. The first method is based on the TD concept. It uses the image autocorrelation matrix and the target signature in the detection space (DS) for FS. Features that increase the first-norm distance between the target energy and the mean energy of the background in DS are selected as optimal. The other two methods use background modeling via image clustering. The cluster mean spectra, along with the target spectrum, are then transferred into DS. Orthogonal subspace projection distance (OSPD) and first-norm distance (FND) are used as two FS criteria to select optimal features. Two datasets, HyMap RIT and SIM.GA, are used for the experiments. Several measures, i.e., true positives (TPs), false alarms (FAs), target detection accuracy (TDA), total negative score (TNS), and the receiver operating characteristics (ROC) area under the curve (AUC) are employed to evaluate the proposed methods and to investigate the impact of FS on the TD performance. The experimental results show that our proposed FS methods, as compared with five existing FS methods, have improving impacts on common target detectors and help them to yield better results.

scholarly journals Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yongqi Guo ◽  
Yuxu Lu ◽  
Yu Guo ◽  
Ryan Wen Liu ◽  
Kwok Tai Chui
Keyword(s):  
Target Detection ◽  
Water Surface ◽  
Data Augmentation ◽  
Weather Conditions ◽  
Detection Performance ◽  
Feature Representation ◽  
Maritime Transportation ◽  
Detection Accuracy ◽  
Visibility Enhancement ◽  
Degraded Images

The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.

Measuring Impact of Stress in Sandwich Generation Caring for Demented Parents

GeroPsych ◽  
2014 ◽  
Vol 27 (4) ◽  
pp. 171-179 ◽  
Author(s):  
Laurence M. Solberg ◽  
Lauren B. Solberg ◽  
Emily N. Peterson
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Social Worker ◽  
Resource Availability ◽  
Well Being ◽  
Sandwich Generation ◽  
Item Questionnaire ◽  
Significant Difference ◽  
Adult Caregivers ◽  
The Impact

Stress in caregivers may affect the healthcare recipients receive. We examined the impact of stress experienced by 45 adult caregivers of their elderly demented parents. The participants completed a 32-item questionnaire about the impact of experienced stress. The questionnaire also asked about interventions that might help to reduce the impact of stress. After exploratory factor analysis, we reduced the 32-item questionnaire to 13 items. Results indicated that caregivers experienced stress, anxiety, and sadness. Also, emotional, but not financial or professional, well-being was significantly impacted. There was no significant difference between the impact of caregiver stress on members from the sandwich generation and those from the nonsandwich generation. Meeting with a social worker for resource availability was identified most frequently as a potentially helpful intervention for coping with the impact of stress.

Confirmatory Factor Analysis of the Impact of Event Scale - Revised

2007 ◽  
Author(s):  
Robert Orazem ◽  
Claire Hebenstreit ◽  
Daniel King ◽  
Lynda King ◽  
Arieh Shalev ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Impact Of Event Scale ◽  
Event Scale ◽  
Confirmatory Factor ◽  
The Impact

Role of Brand experience in Fashion brands

2019 ◽  
Vol 118 (1) ◽  
pp. 57-64
Author(s):  
G. Aiswarya ◽  
Dr. Jayasree Krishnan
Keyword(s):  
Factor Analysis ◽  
Purchase Intention ◽  
Great Influence ◽  
Brand Experience ◽  
Brand Preference ◽  
Mediating Role ◽  
Confirmatory Factor ◽  
Selling Strategies ◽  
The Impact

Traditionally the products were pushed into the hands of customers by production and selling strategies; then the marketing strategy evolved which gained momentum by understanding the customer needs and developing products satisfying those needs. This strategy is most prevalent and what should be done to stand up in this most competitive scenario? The answer to this key question is to create an experience. The customers now also seek good experiences than other benefits. Brand experience has gained more attention, especially fashion brands. Previous studies demonstrate the role of the brand experience in brand equity and other consumer behavior constructs. But very little is known about the impact of brand experiences on fashion brands. The aim of this study is to develop a model which makes our understanding better about the role of Brand preference and Brand experience and its influence on purchase intention of the brand. An initial exploratory study is conducted using a focus group to generate items for the study. The items, thus generated are prepared in the form of a questionnaire and samples were collected.  Exploratory factor analysis is conducted and the reliability of the constructs is determined. These constructs are loaded onto AMOS to perform Confirmatory factor analysis. The results confirmed the scales used. We also noticed that Brand preference has a great influence on the Brand experience. Thereby the finding supports the role of the brand experience which tends to have a mediating role in influencing the purchase intention.

Prospects of spring wheat cultivation in the conditions of arid Volga region

2020 ◽  
pp. 28-33
Author(s):  
Valery Genadievich Popov ◽  
Andrey Vladimirovich Panfilov ◽  
Yuriy Vyacheslavovich Bondarenko ◽  
Konstantin Mikhailovich Doronin ◽  
Evgeny Nikolaevih Martynov ◽  
...  
Keyword(s):  
Spring Wheat ◽  
Soil Layer ◽  
Weather Conditions ◽  
Volga Region ◽  
Mineral Fertilizers ◽  
Climate Conditions ◽  
Wheat Cultivation ◽  
Forest Belts ◽  
Clear Differentiation ◽  
The Impact

The article analyzes the experience of the impact of the system of forest belts and mineral fertilizers on the yield of spring wheat, including on irrigated lands. Vegetation irrigation is designed to maintain the humidity of the active soil layer from germination to maturation at the lower level of the optimum-70-75%, and in the phases of tubulation-earing - flowering - 75-80% NV. However, due to the large differences in zones and microzones of soil and climate conditions and due to the weather conditions of individual years, wheat irrigation regimes require a clear differentiation. In the Volga region in the dry autumn rainfalls give the norm of 800-1000 m3/ha, and in saline soils – 1000-1300 and 3-4 vegetation irrigation at tillering, phases of booting, earing and grain formation the norm 600-650 m3/ha. the impact of the system of forest belts, mineral fertilizers on the yield of spring wheat is closely tied to the formation of microclimate at different distances from forest edges.

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