Classification of moving objects in atmospherically degraded video

2012 ◽  
Vol 51 (10) ◽  
pp. 1 ◽  
Author(s):  
Eli Chen ◽  
Oren Haik ◽  
Yitzhak Yitzhaky
Keyword(s):  
Moving Objects

scholarly journals Work-Related Fatalities Involving Children in New Zealand, 1999–2014

Children ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 4
Author(s):  
Rebbecca Lilley ◽  
Bronwen McNoe ◽  
Gabrielle Davie ◽  
Brandon de Graaf ◽  
Tim Driscoll
Keyword(s):  
New Zealand ◽  
Moving Objects ◽  
Unintentional Injury ◽  
Child Work ◽  
Workplace Hazards ◽  
Work Related ◽  
International Classification Of Disease ◽  
Injury Mortality ◽  
Work Related Injury

In high income countries, children under 15 years of age are exposed to workplace hazards when they visit or live on worksites or participate in formal or informal work. This study describes the causes and circumstances of unintentional child work-related fatal injuries (child WRFI) in New Zealand. Potential cases were identified from the Mortality Collection using International Classification of Disease external cause codes: these were matched to Coronial records and reviewed for work-relatedness. Data were abstracted on the socio-demographic, employment and injury-related circumstances. Of the 1335 unintentional injury deaths in children from 1999 through 2014, 206 (15%) were identified as dying from a work-related injury: 9 workers and 197 bystanders—the majority involving vehicle crashes or being stuck by moving objects in incidents occurring on farms or public roads. Those at highest risk were males, preschoolers, and those of Māori or European ethnicity. Work made a notable contribution to the burden of unintentional fatal injury in children with most deaths highly preventable, largely by adult intervention and legislation. To address the determinants of child WRFI greater attention on rural farm and transport settings would result in a significant reduction in the injury mortality rates of New Zealand children.

scholarly journals IMPROVEMENT OF FIDELITY OF MOVING OBJECTS CLASSIFICATION IN GUARD SIGNALING COMPLEXES WITH SEISMIC SENSORS

2018 ◽  
Vol 8 (4) ◽  
pp. 36-39 ◽  
Author(s):  
Bohdan Volochiy ◽  
Mykhailo Zmysnyi ◽  
Leonid Ozirkovskyy ◽  
Volodomyr Onyschchenko ◽  
Yuriy Salnyk
Keyword(s):  
Performance Indicators ◽  
Moving Objects ◽  
Radio Signal ◽  
Moving Object ◽  
Control Zone ◽  
Majority Principle ◽  
Seismic Sensors ◽  
Probability Of Fixation ◽  
Partial Fulfillment

The effectiveness of guard signaling complexes (GSC), when there is an important validity of the classification of moving objects (MO), is evaluated by the following indexes: probability of GSC task execution; probability of partial fulfillment of the task; probability of user’s “deception”. Accordingly, the performance indicators of the GSC, in turn, depend on the indexes of the functionality of its constituents: probability of fixation of moving object by seismic sensor, probability of correct classification of MO type and probability of receiving radio signal by the system of receiving and displaying information (SRDI). The article describes a discrete-continuous stochastic model of of GSC reaction to moving object crossing control zone, in which three seismic sensors are installed. Majority principle of identifying the type of moving object was used on the receiving part of the complex. A comparative analysis of the effectiveness of guard signaling complexes using one, two and three sensors in control zone are carried out.

Moving Shadow Detection Method Using Logistic Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2724-2727
Author(s):  
Liu Yang ◽  
Jiang Yan Dai ◽  
Miao Qi ◽  
Qing Ji Guan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Moving Objects ◽  
Detection Method ◽  
Experimental Results ◽  
Shadow Detection ◽  
Video Frames ◽  
Moving Shadow Detection

We present a novel moving shadow detection method using logistic regression in this paper. First, several types of features are extracted from pixels in foreground images. Second, the logistic regression model is constructed by random pixels selected from video frames. Finally, for a new frame in one video, we take advantage of the constructed regression model to implement the classification of moving shadows and objects. To verify the performance of the proposed method, we test it on several different surveillance scenes and compare it with some well-known methods. Extensive experimental results indicate that the proposed method not only can separate moving shadows from moving objects accurately, but also is superior to several existing methods.

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