scholarly journals Firearm Recognition Using Convolutional Neural Network

2019 ◽  
pp. 136-141
Author(s):  
T. H. Deepthi ◽  
R. Gaayathri ◽  
S. Shanthosh ◽  
A. Sahaya Gebin ◽  
R. Anitha Nithya
Keyword(s):  
Response Times ◽  
Real Life ◽  
Detection Algorithm ◽  
Automated Detection ◽  
False Alarms ◽  
Effective Response ◽  
Number Of False Alarms ◽  
Single Operator ◽  
Zero Rate ◽  
Housing Estates

Closed circuit television systems (CCTV) are becoming more popular and are being deployed in many offices, housing estates and in the most public spaces. Monitoring systems have been implemented in many foreign cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by the human factors. The projects focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm is visible in the image and also have focussed on limiting the number of false alarms, in order to allow a real life application of the system. Managed to propose a version of a firearm detection algorithm that offers a near zero rate of false alarms and have shown that it is possible to create a system that are capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

scholarly journals Automatic Detection of Crimes Captured in CCTV Images for Safety of Senior Citizens

2021 ◽  
pp. 291-296
Author(s):  
Baswaraju Swathi ◽  
Anitha B ◽  
Disha Singh ◽  
Divya Shree M ◽  
Kushala R
Keyword(s):  
Response Times ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Effective Response ◽  
Dangerous Situation ◽  
Monitoring Process ◽  
Cctv Surveillance ◽  
Housing Estates ◽  
E Mail ◽  
Detection And Recognition

Closed circuit television systems (CCTV) are getting widely popular and are being deployed in many workspaces, housing estates and in most public spaces. Efficiency of CCTV surveillance can be improved by incorporation of image processing and object detection algorithm into monitoring process. In this project, we specialize in the task of automated detection and recognition of dangerous incidents for CCTV systems. We propose solutions that are able to alert the human operator when a weapon is visible in the image through e-mail. We have shown that it's possible to make a system that's capable of an early warning during a dangerous situation, which can cause faster and more effective response times and reduce in the number of potential victims. Face Detection and Face recognition of individuals is an intricate problem which has garnered much attention during recent years because of its ever-increasing applications in numerous fields. In this project the facial detection has been carried out using Viola Jones algorithm.

Algorithms for detecting broadband signals with a constant level of false alarms

2021 ◽  
Author(s):  
I.F. Lozovskiy
Keyword(s):  
Detection Threshold ◽  
Signal Amplitude ◽  
Rayleigh Distribution ◽  
Training Sample ◽  
Detection Algorithm ◽  
Constant Level ◽  
False Alarms ◽  
Interference Power ◽  
Number Of False Alarms ◽  
Distribution Type

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

scholarly journals FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

2021 ◽  
Vol 38 (5) ◽  
pp. 1495-1501
Author(s):  
Hui Huang ◽  
Zhe Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
Real Time ◽  
Large Scale ◽  
Real Life ◽  
Detection Algorithm ◽  
False Alarms ◽  
License Plate ◽  
Embedded Devices ◽  
License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

scholarly journals Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1357
Author(s):  
Simon Scheurer ◽  
Janina Koch ◽  
Martin Kucera ◽  
Hȧkon Bryn ◽  
Marcel Bärtschi ◽  
...  
Keyword(s):  
Real Life ◽  
Fall Detection ◽  
Detection Algorithm ◽  
False Alarms ◽  
Second Phase ◽  
Two Phase ◽  
Motion Data ◽  
Technical Validation ◽  
Young Subjects ◽  
Risk Of Fall

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.

scholarly journals Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Glen Debard ◽  
Marc Mertens ◽  
Toon Goedemé ◽  
Tinne Tuytelaars ◽  
Bart Vanrumste
Keyword(s):  
False Alarm ◽  
Detection System ◽  
Real Life ◽  
Fall Detection ◽  
Training Data ◽  
False Alarms ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Real Life Data ◽  
Precision Recall Curve

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

Gender Differences in Criterion Task Set Performance and Subjective Ratings

1988 ◽  
Vol 32 (14) ◽  
pp. 848-852
Author(s):  
Betina Schlegel ◽  
Robert E. Schlegel ◽  
Kirby Gilliland
Keyword(s):  
Gender Differences ◽  
Large Scale ◽  
Response Times ◽  
Memory Search ◽  
False Alarms ◽  
Subjective Ratings ◽  
Task Set ◽  
Criterion Task ◽  
Number Of False Alarms ◽  
High Level

This paper summarizes gender differences in performing various elements of the Criterion Task Set. Performance data and Subjective Workload Assessment Technique ratings were analyzed for 28 men and 28 women who participated in a large-scale CTS validation study. In general, women tended to perform slightly better than men on the majority of tasks. In particular, performance by women was better on Grammatical Reasoning, Linguistic Processing, Mathematical Processing, and Memory Search. Response times on Probability Monitoring were faster for women but at the expense of a greater number of False Alarms. Men performed better only on the high level of Continuous Recall and the medium level of Unstable Tracking. Women tended to give lower subjective ratings than men to those tasks with a high memory component and gave higher ratings than men to those tasks involving input/output and spatial elements.

scholarly journals The algorithm of nighttime pedestrian detection in intelligent surveillance for renewable energy power stations

2020 ◽  
Vol 38 (5) ◽  
pp. 2019-2036 ◽  
Author(s):  
Bao Peng ◽  
Zhi-Bin Chen ◽  
Erkang Fu ◽  
Zi-Chuan Yi
Keyword(s):  
Support Vector Machine ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Support Vector ◽  
False Alarms ◽  
Image Preprocessing ◽  
Intelligent Surveillance ◽  
Power Stations ◽  
Number Of False Alarms

Intelligent surveillance is an important management method for the construction and operation of power stations such as wind power and solar power. The identification and detection of equipment, facilities, personnel, and behaviors of personnel are the key technology for the ubiquitous electricity The Internet of Things. This paper proposes a video solution based on support vector machine and histogram of oriented gradient (HOG) methods for pedestrian safety problems that are common in night driving. First, a series of image preprocessing methods are used to optimize night images and detect lane lines. Second, an image is divided into intelligent regions to be adapted to different road environments. Finally, the HOG and support vector machine methods are used to optimize the pedestrian image on a Linux system, which reduces the number of false alarms in pedestrian detection and the workload of the pedestrian detection algorithm. The test results show that the system can successfully detect pedestrians at night. With image preprocessing optimization, the correct rate of nighttime pedestrian detection can be significantly improved, and the correct rate of detection can reach 92.4%. After the division area is optimized, the number of false alarms decreases significantly, and the average frame rate of the optimized video reaches 28 frames per second.

scholarly journals Identification of Soft Fall based on Falling State Occurrences

2021 ◽  
Vol 15 (3) ◽  
pp. 324-332
Author(s):  
Thein Gi Kyaw ◽  
Anant Choksuriwong ◽  
Nikom Suvonvorn
Keyword(s):  
Daily Living ◽  
Real Life ◽  
Fall Detection ◽  
The Elderly ◽  
Learning Model ◽  
Experimental Results ◽  
False Alarms ◽  
Detection Techniques ◽  
Number Of False Alarms ◽  
Acceleration Data

Fall detection techniques for helping the elderly were developed based on identifying falling states using simulated falls. However, some real-life falling states were left undetected, which led to this work on analysing falling states. The aim was to find the differences between active daily living and soft falls where falling states were undetected. This is the first consideration to be based on the threshold-based algorithms using the acceleration data stored in an activity database. This study addresses soft falls in addition to the general falls based on two falling states. Despite the number of false alarms being higher rising from 18.5% to 56.5%, the sensitivity was increased from 52% to 92.5% for general falls, and from 56% to 86% for soft falls. Our experimental results show the importance of state occurrence for soft fall detection, and will be used to build a learning model for soft fall detection.

Animal Recognition and Eye Movements

2010 ◽  
Vol 57 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Toby J. Lloyd-Jones ◽  
Juergen Gehrke ◽  
Jason Lauder
Keyword(s):  
Eye Movements ◽  
Response Times ◽  
Real Life ◽  
Object Orientation ◽  
Decision Task ◽  
Line Drawings ◽  
Object Decision ◽  
Individual Features ◽  
And Performance

We assessed the importance of outline contour and individual features in mediating the recognition of animals by examining response times and eye movements in an animal-object decision task (i.e., deciding whether or not an object was an animal that may be encountered in real life). There were shorter latencies for animals as compared with nonanimals and performance was similar for shaded line drawings and silhouettes, suggesting that important information for recognition lies in the outline contour. The most salient information in the outline contour was around the head, followed by the lower torso and leg regions. We also observed effects of object orientation and argue that the usefulness of the head and lower torso/leg regions is consistent with a role for the object axis in recognition.

Participation and spatial transformation in large housing estates in Poland. Case study of Ursynów Północny

2018 ◽  
Vol 60 (1) ◽  
pp. 55-65
Author(s):  
Krystyna Ilmurzyńska
Keyword(s):  
Basic Assumption ◽  
Real Life ◽  
Business Environment ◽  
Spatial Transformation ◽  
Reciprocal Relationships ◽  
Spatial Transformations ◽  
Large Housing Estates ◽  
Existing Housing ◽  
Housing Estates

Abstract This article investigates the suitability of traditional and participatory planning approaches in managing the process of spatial development of existing housing estates, based on the case study of Warsaw’s Ursynów Północny district. The basic assumption of the article is that due to lack of government schemes targeted at the restructuring of large housing estates, it is the business environment that drives spatial transformations and through that shapes the development of participation. Consequently the article focuses on the reciprocal relationships between spatial transformations and participatory practices. Analysis of Ursynów Północny against the background of other estates indicates that it presents more endangered qualities than issues to be tackled. Therefore the article focuses on the potential of the housing estate and good practices which can be tracked throughout its lifetime. The paper focuses furthermore on real-life processes, addressing the issue of privatisation, development pressure, formal planning procedures and participatory budgeting. In the conclusion it attempts to interpret the existing spatial structure of the estate as a potential framework for a participatory approach.

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