ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE

2004 ◽  
Vol 01 (02) ◽  
pp. 169-189
Author(s):  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Real Life ◽  
Visual Surveillance ◽  
Longest Common Subsequence ◽  
Human Walking ◽  
Support Vector ◽  
Surveillance Systems ◽  
Boundary Values ◽  
Variable Boundary

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

CVSS

2019 ◽  
pp. 19-32
Author(s):  
Lei Zhou ◽  
Wei Qi Yan ◽  
Yun Shu ◽  
Jian Yu
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Virtual Machine ◽  
Surveillance System ◽  
Video Stream ◽  
Visual Surveillance ◽  
Video Transcoding ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
And Storage

A large amount of surveillance videos and images need sufficient storage. In this article, an architecture of cloud-based surveillance systems and its modules will be designed, the Cloud-based Visual Surveillance System (CVSS) will be implemented on a private cloud using a Virtual Machine (VM). The users are able to link their cameras to the CVSS system so that the goal of this design can be achieved. The authors' CVSS system is able to push notification messages of captured videos to receivers, and their users could receive a surveillance video along with its events. The CVSS system fully makes use of the merits of cloud computing, which make it more advanced as stated in the evaluation section of this article. The contributions of this article are to be implemented in the CVSS system with: (1) video stream input, (2) intelligent visual surveillance, (3) real-time video transcoding and storage, (4) message pushing and media streaming output.

scholarly journals Detecting Human and Classification of Gender using Facial Images MSIFT Features Based GSVM

2019 ◽  
Vol 8 (3) ◽  
pp. 1466-1471 ◽  
Keyword(s):  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Human Detection ◽  
Support Vector ◽  
Time Data ◽  
Processing Application ◽  
Wide Range ◽  
Human Image

Classification of gender using face recognition system is an essential concept for different types of applications in human-computer interaction and computer-aided related applications. It defines a wide range of features from human images to detect male, female and others using real-time data. There are different machine learning approaches were implemented to classify gender and also detects other images during the classification phase, which are not humans based on features extracted from human images datasets. All these existing techniques mostly depend on controlled conditions like features and other representations of the human image. Because of significant and uncertain variations of a particular image, it may be a challenging task in gender classification for real-time image processing application, whether it is male, female and others. So that in this document, we propose a Human detection and Face based gender Recognition System (HDFGR); to investigate male or female classification on real life faces using real world face databases. Our proposed approach consists Multi-Scale Invariant Feature Transform (MSIFT) to describes faces and Gaussian distance-based support vector machine (GSVM) classifier is used to classify gender and objects, i.e. male, female and other from features extracted human image datasets. We obtain an experimental performance of 98.7% by applying DSVM with boosted MSIFT features. Our proposed approach gives better classification accuracy and other performance parameters compared to different existing approaches with benchmark and evaluation of possible databases.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

Grenzüberschreitendes real-time Surveillance-Systems für Infektionskrankheiten

2011 ◽  
Vol 73 (03) ◽  
Author(s):  
H Freund ◽  
V Bochat ◽  
H ter Waarbeek
Keyword(s):  
Real Time ◽  
Surveillance Systems

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Malaria elimination in India requires additional surveillance mechanisms

2021 ◽  
Author(s):  
Manju Rahi ◽  
Payal Das ◽  
Amit Sharma
Keyword(s):  
Real Time ◽  
Malaria Elimination ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Malaria Surveillance ◽  
Aggregated Data ◽  
Malaria Burden ◽  
The Core ◽  
Reporting Systems ◽  
Geographic Regions

Abstract Malaria surveillance is weak in high malaria burden countries. Surveillance is considered as one of the core interventions for malaria elimination. Impressive reductions in malaria-associated morbidity and mortality have been achieved across the globe, but sustained efforts need to be bolstered up to achieve malaria elimination in endemic countries like India. Poor surveillance data become a hindrance in assessing the progress achieved towards malaria elimination and in channelizing focused interventions to the hotspots. A major obstacle in strengthening India’s reporting systems is that the surveillance data are captured in a fragmented manner by multiple players, in silos, and is distributed across geographic regions. In addition, the data are not reported in near real-time. Furthermore, multiplicity of malaria data resources limits interoperability between them. Here, we deliberate on the acute need of updating India’s surveillance systems from the use of aggregated data to near real-time case-based surveillance. This will help in identifying the drivers of malaria transmission in any locale and therefore will facilitate formulation of appropriate interventional responses rapidly.

scholarly journals A hierarchical clustering method for random intervals based on a similarity measure

2021 ◽  
Author(s):  
Ana Belén Ramos-Guajardo
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Real Life ◽  
Stopping Criterion ◽  
Clustering Method ◽  
Bootstrap Test ◽  
Empirical Performance ◽  
Random Intervals ◽  
Expected Values

AbstractA new clustering method for random intervals that are measured in the same units over the same group of individuals is provided. It takes into account the similarity degree between the expected values of the random intervals that can be analyzed by means of a two-sample similarity bootstrap test. Thus, the expectations of each pair of random intervals are compared through that test and a p-value matrix is finally obtained. The suggested clustering algorithm considers such a matrix where each p-value can be seen at the same time as a kind of similarity between the random intervals. The algorithm is iterative and includes an objective stopping criterion that leads to statistically similar clusters that are different from each other. Some simulations to show the empirical performance of the proposal are developed and the approach is applied to two real-life situations.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

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