scholarly journals LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8501
Author(s):  
Abid Mehmood
Keyword(s):  
Cost Effective ◽  
Abnormal Behavior ◽  
Model Complexity ◽  
Surveillance Systems ◽  
Lightweight Framework ◽  
Efficient Detection ◽  
Cost Effective Method ◽  
Abnormal Behavior Detection ◽  
Motion Features ◽  
Public Datasets

The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the literature that detect different anomalies by using the details of motion features associated with different actions. To enable the efficient detection of anomalies, alongside characterizing the specificities involved in features related to each behavior, the model complexity leading to computational expense must be reduced. This paper provides a lightweight framework (LightAnomalyNet) comprising a convolutional neural network (CNN) that is trained using input frames obtained by a computationally cost-effective method. The proposed framework effectively represents and differentiates between normal and abnormal events. In particular, this work defines human falls, some kinds of suspicious behavior, and violent acts as abnormal activities, and discriminates them from other (normal) activities in surveillance videos. Experiments on public datasets show that LightAnomalyNet yields better performance comparative to the existing methods in terms of classification accuracy and input frames generation.

Applied-Information Technology in Abnormal Detection for Surveillance Systems

2014 ◽  
Vol 1046 ◽  
pp. 266-269
Author(s):  
Feng Xu
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Detection System ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Criminal Cases ◽  
Abnormal Detection ◽  
Abnormal Behavior Detection ◽  
Artificial Intelligence Technology ◽  
Function Module

In recent years, video surveillance has become more and more important for enhanced security and it is indispensable technology for fighting against all types of crime with the construction of sky-net in China. Abnormal detection is the focus of intelligent video surveillance and the information of abnormal behavior can be used in the investigation of criminal cases, which combines computer vision and artificial intelligence technology and has wide application prospect in public security work. In this paper, first the current research situation of the intelligent surveillance system is introduced. Then the category of abnormal behavior detection is expounded. Finally the function module of abnormal detection system is designed and the key technology of moving target detection, target tracking and abnormality judgment is discussed in view of the actual situation of surveillance system in criminal cases.

scholarly journals Abnormal Human Behavior Detection in Videos: A Review

2021 ◽  
Vol 50 (3) ◽  
pp. 522-545
Author(s):  
Huiyu Mu ◽  
Ruizhi Sun ◽  
Gang Yuan ◽  
Yun Wang
Keyword(s):  
Video Surveillance ◽  
Human Behavior ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Behavior Detection ◽  
Event Modeling ◽  
Semantic Inference ◽  
Abnormal Behavior Detection ◽  
Private And Public ◽  
New Perspective

Modeling human behavior patterns for detecting the abnormal event has become an important domain in recentyears. A lot of efforts have been made for building smart video surveillance systems with the purpose ofscene analysis and making correct semantic inference from the video moving target. Current approaches havetransferred from rule-based to statistical-based methods with the need of efficient recognition of high-levelactivities. This paper presented not only an update expanding previous related researches, but also a study coveredthe behavior representation and the event modeling. Especially, we provided a new perspective for eventmodeling which divided the methods into the following subcategories: modeling normal event, predictionmodel, query model and deep hybrid model. Finally, we exhibited the available datasets and popular evaluationschemes used for abnormal behavior detection in intelligent video surveillance. More researches will promotethe development of abnormal human behavior detection, e.g. deep generative network, weakly-supervised. It isobviously encouraged and dictated by applications of supervising and monitoring in private and public space.The main purpose of this paper is to widely recognize recent available methods and represent the literature ina way of that brings key challenges into notice.

scholarly journals Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

2021 ◽  
Vol 11 (8) ◽  
pp. 3523
Author(s):  
Abid Mehmood
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Data ◽  
Abnormal Behavior ◽  
Surveillance Systems ◽  
Detection Rates ◽  
Abnormal Behavior Detection ◽  
General Action ◽  
Increasing Demand ◽  
Abnormal Events

The increasing demand for surveillance systems has resulted in an unprecedented rise in the volume of video data being generated daily. The volume and frequency of the generation of video streams make it both impractical as well as inefficient to manually monitor them to keep track of abnormal events as they occur infrequently. To alleviate these difficulties through intelligent surveillance systems, several vision-based methods have appeared in the literature to detect abnormal events or behaviors. In this area, convolutional neural networks (CNNs) have also been frequently applied due to their prevalence in the related domain of general action recognition and classification. Although the existing approaches have achieved high detection rates for specific abnormal behaviors, more inclusive methods are expected. This paper presents a CNN-based approach that efficiently detects and classifies if a video involves the abnormal human behaviors of falling, loitering, and violence within uncrowded scenes. The approach implements a two-stream architecture using two separate 3D CNNs to accept a video and an optical flow stream as input to enhance the prediction performance. After applying transfer learning, the model was trained on a specialized dataset corresponding to each abnormal behavior. The experiments have shown that the proposed approach can detect falling, loitering, and violence with an accuracy of up to 99%, 97%, and 98%, respectively. The model achieved state-of-the-art results and outperformed the existing approaches.

CAD Of Anticorrosive Protection Of Steel Structures

2019 ◽  
pp. 18-23
Keyword(s):  
Protective Coating ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Cost Effective ◽  
Operating Conditions ◽  
Computational Tool ◽  
Protection Method ◽  
Cost Effective Method ◽  
The Cost ◽  
Aided Design

The choice of cost-effective method of anticorrosive protection of steel structures is an urgent and time consuming task, considering the significant number of protection ways, differing from each other in the complex of technological, physical, chemical and economic characteristics. To reduce the complexity of solving this problem, the author proposes a computational tool that can be considered as a subsystem of computer-aided design and used at the stage of variant and detailed design of steel structures. As a criterion of the effectiveness of the anti-corrosion protection method, the cost of the protective coating during the service life is accepted. The analysis of existing methods of steel protection against corrosion is performed, the possibility of their use for the protection of the most common steel structures is established, as well as the estimated period of effective operation of the coating. The developed computational tool makes it possible to choose the best method of protection of steel structures against corrosion, taking into account the operating conditions of the protected structure and the possibility of using a protective coating.

Monitoring effluent quality in hypertrophic wastewater reservoirs using remote sensing

1996 ◽  
Vol 33 (8) ◽  
pp. 23-29 ◽  
Author(s):  
I. Dor ◽  
N. Ben-Yosef
Keyword(s):  
Water Quality ◽  
Theoretical Model ◽  
Principal Component ◽  
Cost Effective ◽  
Operational Parameters ◽  
Effluent Quality ◽  
Optical Images ◽  
Routine Monitoring ◽  
Cost Effective Method ◽  
Wastewater Quality

About one hundred and fifty wastewater reservoirs store effluents for irrigation in Israel. Effluent qualities differ according to the inflowing wastewater quality, the degree of pretreatment and the operational parameters. Certain aspects of water quality like concentration of organic matter, suspended solids and chlorophyll are significantly correlated with the water column transparency and colour. Accordingly optical images of the reservoirs obtained from the SPOT satellite demonstrate pronounced differences correlated with the water quality. The analysis of satellite multispectral images is based on a theoretical model. The model calculates, using the radiation transfer equation, the volume reflectance of the water body. Satellite images of 99 reservoirs were analyzed in the chromacity space in order to classify them according to water quality. Principal Component Analysis backed by the theoretical model increases the method sensitivity. Further elaboration of this approach will lead to the establishment of a time and cost effective method for the routine monitoring of these hypertrophic wastewater reservoirs.

A Cost-Effective Method to Prepare Pure Vildagliptin

2013 ◽  
Vol 10 (3) ◽  
pp. 159-163 ◽  
Author(s):  
Jun Peng ◽  
Yue Feng ◽  
Zhu Tao ◽  
Yingjie Chen ◽  
Xiangnan Hu
Keyword(s):  
Cost Effective ◽  
Cost Effective Method

scholarly journals Computer-supported Detection of M-Components and Evaluation of Immunoglobulins after Capillary Electrophoresis

2001 ◽  
Vol 47 (1) ◽  
pp. 110-117 ◽  
Author(s):  
Magnus Jonsson ◽  
Joyce Carlson ◽  
Jan-Olof Jeppsson ◽  
Per Simonsson
Keyword(s):  
Capillary Electrophoresis ◽  
Decision Support ◽  
Protein Separation ◽  
Serum Proteins ◽  
Cost Effective ◽  
Clinical Samples ◽  
Serum Samples ◽  
Computerized Decision Support ◽  
Cost Effective Method ◽  
Mathematical Algorithms

Abstract Background: Electrophoresis of serum samples allows detection of monoclonal gammopathies indicative of multiple myeloma, Waldenström macroglobulinemia, monoclonal gammopathy of undetermined significance, and amyloidosis. Present methods of high-resolution agarose gel electrophoresis (HRAGE) and immunofixation electrophoresis (IFE) are manual and labor-intensive. Capillary zone electrophoresis (CZE) allows rapid automated protein separation and produces digital absorbance data, appropriate as input for a computerized decision support system. Methods: Using the Beckman Paragon CZE 2000 instrument, we analyzed 711 routine clinical samples, including 95 monoclonal components (MCs) and 9 cases of Bence Jones myeloma, in both the CZE and HRAGE systems. Mathematical algorithms developed for the detection of monoclonal immunoglobulins (MCs) in the γ- and β-regions of the electropherogram were tested on the entire material. Additional algorithms evaluating oligoclonality and polyclonal concentrations of immunoglobulins were also tested. Results: CZE electropherograms corresponded well with HRAGE. Only one IgG MC of 1 g/L, visible on HRAGE, was not visible after CZE. Algorithms detected 94 of 95 MCs (98.9%) and 100% of those visible after CZE. Of 607 samples lacking an MC on HRAGE, only 3 were identified by the algorithms (specificity, 99%). Algorithms evaluating total gammaglobulinemia and oligoclonality also identified several cases of Bence Jones myeloma. Conclusions: The use of capillary electrophoresis provides a modern, rapid, and cost-effective method of analyzing serum proteins. The additional option of computerized decision support, which provides rapid and standardized interpretations, should increase the clinical availability and usefulness of protein analyses in the future.

scholarly journals CO$$_{2}$$ Convection in Hydrocarbon Under Flowing Conditions

2021 ◽  
Author(s):  
Trine S. Mykkeltvedt ◽  
Sarah E. Gasda ◽  
Tor Harald Sandve
Keyword(s):  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Flow Behavior ◽  
Cost Effective ◽  
Fine Grid ◽  
Cost Effective Method ◽  
Gravity Effects ◽  
Petroleum Resources ◽  
Nearby Point ◽  
Diffusive Processes

AbstractCarbon-neutral oil production is one way to improve the sustainability of petroleum resources. The emissions from produced hydrocarbons can be offset by injecting capture CO$$_{2}$$ 2 from a nearby point source into a saline aquifer for storage or a producing oil reservoir. The latter is referred to as enhanced oil recovery (EOR) and would enhance the economic viability of CO$$_{2}$$ 2 sequestration. The injected CO$$_{2}$$ 2 will interact with the oil and cause it to flow more freely within the reservoir. Consequently, the overall recovery of oil from the reservoir will increase. This enhanced oil recovery (EOR) technique is perceived as the most cost-effective method for disposing captured CO$$_{2}$$ 2 emissions and has been performed for many decades with the focus on oil recovery. The interaction between existing oil and injected CO$$_{2}$$ 2 needs to be fully understood to effectively manage CO$$_{2}$$ 2 migration and storage efficiency. When CO$$_{2}$$ 2 and oil mix in a fully miscible setting, the density can change non-linearly and cause density instabilities. These instabilities involve complex convective-diffusive processes, which are hard to model and simulate. The interactions occur at the sub-centimeter scale, and it is important to understand its implications for the field scale migration of CO$$_{2}$$ 2 and oil. In this work, we simulate gravity effects, namely gravity override and convective mixing, during miscible displacement of CO$$_{2}$$ 2 and oil. The flow behavior due to the competition between viscous and gravity effects is complex, and can only be accurately simulated with a very fine grid. We demonstrate that convection occurs rapidly, and has a strong effect on breakthrough of CO$$_{2}$$ 2 at the outlet. This work for the first time quantifies these effects for a simple system under realistic conditions.

Sign in / Sign up

Export Citation Format

Share Document