55 research outputs found

    Object Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects

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    Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques

    Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

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    BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

    Object Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects

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    185-191Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques
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