983 resultados para People Detection
Resumo:
In this paper we present a method for real-time detection and tracking of people in video captured by a depth camera. For each object to be assessed, an ordered sequence of values that represents the distances between its center of mass to the boundary points is calculated. The recognition is based on the analysis of the total distance value between the above sequence and some pre-defined human poses, after apply the Dynamic Time Warping. This similarity approach showed robust results in people detection.
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In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.
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This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ""foreground mass"" above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS`06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method. (C) 2010 Elsevier B.V. All rights reserved.
Resumo:
[EN]Detecting people is a key capability for robots that operate in populated environments. In this paper, we have adopted a hierarchical approach that combines classifiers created using supervised learning in order to identify whether a person is in the view-scope of the robot or not. Our approach makes use of vision, depth and thermal sensors mounted on top of a mobile platform.
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The Casa da Música Foundation, responsible for the management of Casa da Música do Porto building, has the need to obtain statistical data related to the number of building’s visitors. This information is a valuable tool for the elaboration of periodical reports concerning the success of this cultural institution. For this reason it was necessary to develop a system capable of returning the number of visitors for a requested period of time. This represents a complex task due to the building’s unique architectural design, characterized by very large doors and halls, and the sudden large number of people that pass through them in moments preceding and proceeding the different activities occurring in the building. To achieve the technical solution for this challenge, several image processing methods, for people detection with still cameras, were first studied. The next step was the development of a real time algorithm, using OpenCV libraries and computer vision concepts,to count individuals with the desired accuracy. This algorithm includes the scientific and technical knowledge acquired in the study of the previous methods. The themes developed in this thesis comprise the fields of background maintenance, shadow and highlight detection, and blob detection and tracking. A graphical interface was also built, to help on the development, test and tunning of the proposed system, as a complement to the work. Furthermore, tests to the system were also performed, to certify the proposed techniques against a set of limited circumstances. The results obtained revealed that the algorithm was successfully applied to count the number of people in complex environments with reliable accuracy.
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In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.
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A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.
Resumo:
BACKGROUND: Among young people, about one in three females and one in five males report experiencing emotional distress but 65-95% of them do not receive help from health professionals. AIM: To assess the differences among young people who seek help and those who do not seek help for their psychological problems, considering the frequency of consultations to their GP and their social resources. DESIGN OF STUDY: School survey. SETTING: Post-mandatory school. METHOD: Among a Swiss national representative sample of 7429 students and apprentices (45.6% females) aged 16-20 years, 1931 young people reported needing help for a problem of depression/sadness (26%) and were included in the study. They were divided into those who sought help (n = 256) and those who did not (n = 1675), and differences between them were assessed. RESULTS: Only 13% of young people needing help for psychological problems consulted for that reason and this rate was positively associated with the frequency of consultations to the GP. However, 80% of young people who did not consult for psychological problems visited their GP at least once during the previous year. Being older or a student, having a higher depression score, or a history of suicide attempt were linked with a higher rate of help seeking. Moreover, confiding in adults positively influenced the rate of help seeking. CONCLUSION: The large majority of young people reporting psychological problems do not seek help, although they regularly consult their GP. While young people have difficulties in tackling issues about mental health, GPs could improve the situation by systematically inquiring about this issue.
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Background. Falls and fear of falling present a major risk to older people as both can affect their quality of life and independence. Mobile assistive technologies (AT) fall detection devices may maximise the potential for older people to live independently for as long as possible within their own homes by facilitating early detection of falls. Aims. To explore the experiences and perceptions of older people and their carers as to the potential of a mobile falls detection AT device. Methods. Nine focus groups with 47 participants including both older people with a range of health conditions and their carers. Interviews were audio recorded, transcribed verbatim, and thematically analysed. Results. Four key themes were identified relating to participants’ experiences and perceptions of falling and the potential impact of a mobile falls detector: cause of falling, falling as everyday vulnerability, the environmental context of falling, and regaining confidence and independence by having a mobile falls detector. Conclusion. The perceived benefits of a mobile falls detector may differ between older people and their carers. The experience of falling has to be taken into account when designing mobile assistive technology devices as these may influence perceptions of such devices and how older people utilise them.
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* Chronic heart failure (CHF) is found in 1.5%–2.0% of Australians. Considered rare in people aged less than 45 years, its prevalence increases to over 10% in people aged ≥ 65 years. * CHF is one of the most common reasons for hospital admission and general practitioner consultation in the elderly (≥ 70 years). * Common causes of CHF are ischaemic heart disease (present in > 50% of new cases), hypertension (about two-thirds of cases) and idiopathic dilated cardiomyopathy (around 5%–10% of cases). * Diagnosis is based on clinical features, chest x-ray and objective measurement of ventricular function (eg, echocardiography). Plasma levels of B-type natriuretic peptide (BNP) may have a role in diagnosis, primarily as a test for exclusion. Diagnosis may be strengthened by a beneficial clinical response to treatment(s) directed towards amelioration of symptoms. * Management involves prevention, early detection, amelioration of disease progression, relief of symptoms, minimisation of exacerbations, and prolongation of survival.
Resumo:
Background/Aims: The Mini Addenbrooke’s Cognitive Examination (M-ACE) is the abbreviated version of the widely-used Addenbrooke’s Cognitive Examination (ACE-III), a cognitive screening tool that is used internationally in the assessment of mild cognitive impairment (MCI) and dementia. The objectives of this study were to investigate the diagnostic accuracy of the M-ACE with individuals aged 75 and over to distinguish between those who do and do not have a dementia or MCI, and also to establish whether the cut-off scores recommended by Hsieh et al. (2014) [9] in the original validation study for the M-ACE are optimal for this age group. Methods: The M-ACE was administered to 58 participants (24 with a diagnosis of dementia, 17 with a diagnosis of MCI and 17 healthy controls). The extent to which scores distinguished between groups (dementia, MCI or no diagnosis) was explored using receiver operating characteristic curve analysis. Results: The optimal cut-off for detecting dementia was ≤ 21/30 (score ≤ 21/30 indicating dementia with a sensitivity of 0.95, a specificity of 1 and a positive predictive value of 1) compared to the original higher published cut-off of ≤ 25/30 (sensitivity of 0.95, specificity of 0.70 and a positive predictive value of 0.82 in this sample). Conclusions: The M-ACE has excellent diagnostic accuracy for the detection of dementia in a UK clinical sample. It may be necessary to consider lower cut-offs than those given in the original validation study.
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Objectives: To study the influence of different diagnostic criteria on the prevalence of diabetes mellitus and characteristics of those diagnosed. Design and setting: Retrospective analysis of data from the general-practice-based Australian Diabetes Screening Study (January 1994 to June 1995). Participants: 5911 people with no previous diagnosis of diabetes, two or more symptoms or risk factors for diabetes, a random venous plasma glucose (PG) level > 5.5 mmol/L and a subsequent oral glucose tolerance test (OGTT) result. Main outcome measure: Prevalence of undiagnosed diabetes based on each of three sets of criteria: 1997 criteria of the American Diabetes Association (ADA), 1996 two-step screening strategy of the Australian Diabetes Society (ADS) (modified according to ADA recommendations about lowered diagnostic fasting PG level), and 1999 definition of the World Health Organization (WHO). Results: Prevalence estimates for undiagnosed diabetes using the American (ADA), Australian (ADS) and WHO criteria (95% CI) were 9.4% (8.7%-10.1%), 16.0% (15.3%-16.7%) and 18.1% (17.1%-19.1%), respectively. People diagnosed with diabetes by fasting PG level (common to all sets of criteria) were more likely to be male and younger than those diagnosed only by 2 h glucose challenge PG level (Australian and WHO criteria only). The Australian (ADS) stepwise screening strategy detected 88% of those who met the WHO criteria for diabetes, including about three-quarters of those with isolated post-challenge hyperglycaemia. Conclusion: The WHO criteria (which include an OGTT result) are preferable to the American (ADA) criteria (which rely totally on fasting PG level), as the latter underestimated the prevalence of undiagnosed diabetes by almost a half. The Australian (ADS) strategy identified most of those diagnosed with diabetes by WHO criteria.
Resumo:
Elders lose independence and wellbeing, accompanied by decreased functions in terms of hearing, vision, strength and coordination abilities. These factors contribute to balance difficulties that eventually lead to falls. The injuries due to falls, at this age, are risky, since most of the times may cause a significant – and permanent – decrease of quality of life or, in extreme cases, death. In this context, a fall detection system can bring an added value to assist elderly people.This paper describes a system consisting of a wearable sensor unit, a smartphone and a website. When the sensor detects a fall it sends an alert using the smartphone via Bluetooth 4.0, to notify the family members or stakeholders. The sensor device includes an inertial unit, a barometer, and a temperature and humidity sensor. The website displays the log of previous falls and enables the configuration of emergency contact numbers. The proposed fall detection system is one of multiple components within a larger project under development that offers a holistic perspective on falls; the complete wearable solution will also feature, among others, physical protection (minimizing the impact of falls that occur).
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Epstein Barr Virus (EBV) is transmitted commonly by saliva, but it has been found in genital secretions, which suggests sexual transmission and led researchers to connect EBV and cervical neoplasia. People living with human immunodeficiency virus (HIV) are reported to be at high risk of acquiring genital infections and cervical lesions. To verify the presence of EBV in the genital tract and/or it could affect cervical changes, we analyzed cervical smears from 85 HIV seropositive women for EBV DNA determination. EBV was only detected in two (2.3%) samples. The present study provides neither evidence for EBV as sexually transmitted infection nor discards this possibility.
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Schistosomiasis constitutes a major public health problem, with an estimated 200 million individuals infected worldwide and 700 million people living in risk areas. In Brazil there are areas of high, medium and low endemicity. Studies have shown that in endemic areas with a low prevalence of Schistosoma infection the sensitivity of parasitological methods is clearly reduced. Consequently diagnosis is often impeded due to the presence of false-negative results. The aim of this study is to present the PCR reamplification (Re-PCR) protocol for the detection of Schistosoma mansoni in samples with low parasite load (with less than 100 eggs per gram (epg) of feces). Three methods were used for the lysis of the envelopes of the S. mansoni eggs and two techniques of DNA extraction were carried out. Extracted DNA was quantified, and the results suggested that the extraction technique, which mixed glass beads with a guanidine isothiocyanate/phenol/chloroform (GT) solution, produced good results. PCR reamplification was conducted and detection sensitivity was found to be five eggs per 500 mg of artificially marked feces. The results achieved using these methods suggest that they are potentially viable for the detection of Schistosoma infection with low parasite load.