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ردیف | عنوان | نوع |
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1 |
An automated deep learning based anomaly detection in pedestrian walkways for vulnerable road users safety
یک تشخیص ناهنجاری مبتنی بر یادگیری عمیق در معابر پیاده برای ایمنی کاربران جاده ای آسیب پذیر-2021 Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. Therefore, this paper develops an automated deep learning based anomaly detectiontechnique in pedestrian walkways (DLADT-PW) for vulnerable road user’s safety. The goal of the DLADT-PWmodel is to detect and classify the various anomalies that exist in the pedestrian walkways such as cars, skating, jeep, etc. The DLADT-PW model involves preprocessing as the primary step, which is applied for removing the noise and raise the quality of the image. In addition, mask region convolutional neural network (Mask-RCNN) with densely connected networks (DenseNet) model is employed for the detection process. To ensure the better anomaly detection performance of the DLADT-PW technique, an extensive set of simulations were performed and the outcomes are investigated under distinct aspects. The obtained experimental values confirmed the superior characteristics of the DLADT-PW technique by achieving a maximum detection accuracy. Keywords: Anomaly detection | Pedestrian walkways | Deep learning | Safety | Mask RCNN |
مقاله انگلیسی |
2 |
A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
مروری بر به رسمیت شناختن فعالیتهای چندمنظوره انسان با تأکید ویژه بر طبقه بندی ، کاربردها ، چالشها و جهت های آینده-2021 Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human– robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined.© 2021 Elsevier B.V. All rights reserved. Keywords: Activity recognition | Computer vision | Wearable sensors | Fusion of vision and inertial sensors | Smart-shoes | Multimodality |
مقاله انگلیسی |
3 |
Towards a self-sufficient face verification system
به سمت یک سیستم تأیید چهره خودکفا-2021 The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re- identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness. Keywords: Adaptive biometrics | Video surveillance | Video-to-video face verification | Unsupervised learning | Incremental learning |
مقاله انگلیسی |
4 |
A Complex for Monitoring Transport Infrastructure Facilities Based on Video Surveillance Cameras and Laser Scanners
یک مجتمع برای نظارت حمل و نقل امکانات زیربنایی بر اساس فیلم دوربین های مدار بسته و لیزری اسکنر-2021 The paper is devoted to topical issues of monitoring infrastructure facilities. The aim of the work is to discuss and analyze the most promising and effective methods for monitoring transport infrastructure facilities, developed as a result of recent interdisciplinary studies. After analyzing and combining the results of previous studies, the team of authors presented a model of a complex for monitoring transport infrastructure facilities based on the joint use of video cameras and laser scanners as a permanent and periodic source of information about engineering structures, respectively. Also, the technology of computer vision, neural network algorithms and artificial intelligence methods in relation to the field of monitoring are discussed in the paper. As a result, the structure of an intelligent system for support and decision-making is presented in a graphical form, as well as a block diagram of a stationary monitoring complex based on video surveillance cameras. Conclusions are made about the feasibility and prospects of using such complexes for the needs of monitoring engineering infrastructure facilities, as well as the impact of the development of technologies used in them on world progress in general.© 2021 The Authors.
Keywords: Monitoring of Transport Infrastructure Objects | Laser Scanning | Photo and Video Surveillance Systems | Computer Vision Technology | Neural Network Algorithms | Artificial Intelligence Methods. |
مقاله انگلیسی |
5 |
Development of an AI-based System for Automatic Detection and Recognition of Weapons in Surveillance Videos
توسعه یک سیستم مبتنی بر هوش مصنوعی برای تشخیص و شناسایی خودکار سلاح در نظارت تصویری -2020 Security cameras and video surveillance systems
have become important infrastructures for ensuring safety and
security of the general public. However, the detection of high-risk
situations through these systems are still performed manually in
many cities. The lack of manpower in the security sector and
limited performance of human may result in undetected dangers
or delay in detecting threats, posing risks for the public. In
response, various parties have developed real-time and automated
solutions for identifying risks based on surveillance videos. The
aim of this work is to develop a low-cost, efficient, and artificial
intelligence-based solution for the real-time detection and
recognition of weapons in surveillance videos under different
scenarios. The system was developed based on Tensorflow and
preliminarily tested with a 294-second video which showed 7
weapons within 5 categories, including handgun, shotgun,
automatic rifle, sniper rifle, and submachine gun. At the
intersection over union (IoU) value of 0.50 and 0.75, the system
achieved a precision of 0.8524 and 0.7006, respectively. Keywords : surveillance video | security camera | artificial intelligence | weapon detection | TensorFlow | Single Shot MultiBox Detector |
مقاله انگلیسی |
6 |
Application of fuzzy image restoration in criminal investigation
کاربرد بازسازی تصویر فازی در تحقیقات جنایی-2020 The advancement of science and technology has a positive effect on the development of law disciplines.
The development of algorithms and artificial intelligence also has a certain impact on judicial practice.
Image restoration is a significant technique in image processing. It aims to objectively restore the content
or quality of the original image from the degraded image. Image degradation is always generated in
image transmission, such as distortion, blur. In modern video surveillance system, image restoration is
significant for criminal investigation. However, image restoration based on conventional filter algorithms
cannot achieve satisfactory performance. Thus, we first introduce the image restoration algorithms based
on different degradation model. Then, we propose some applications of fuzzy image restoration in criminal
investigation. We conduct experiments on both degraded images and videos and experimental
results have shown the effectiveness of fuzzy image restoration applying to the criminal investigation. Keywords: Fuzzy image restoration | Image degradation | Criminal investigation |
مقاله انگلیسی |
7 |
A machine learning forensics technique to detect post-processing in digital videos
یک روش پزشکی قانونی برای یادگیری ماشین برای تشخیص پس از پردازش در فیلم های دیجیتال-2020 Technology has brought great benefits to human beings and has served to improve the quality of
life and carry out great discoveries. However, its use can also involve many risks. Examples include
mobile devices, digital cameras and video surveillance cameras, which offer excellent performance and
generate a large number of images and video. These files are generally shared on social platforms and
are exposed to any manipulation, compromising their authenticity and integrity. In a legal process, a
manipulated video can provide the necessary elements to accuse an innocent person of a crime or to
exempt a guilty person from criminal acts. Therefore, it is essential to create robust forensic methods,
which will strengthen the justice administration systems and thus make fair decisions. This paper
presents a novel forensic technique to detect the post-processing of digital videos with MP4, MOV
and 3GP formats. Concretely, detect the social platform and editing program used to execute possible
manipulation attacks. The proposed method is focused on supervised machine learning techniques. To
achieve our goal, we take advantage that the social platforms and editing programs, execute filtering
and compression processes on the videos when they are shared or manipulated. The result of these
transformations leaves a characteristic pattern in the videos that allow us to detect the social platform
or editing program efficiently. Three phases are involved in the method: 1) Dataset preparation; 2) data
features extraction; 3) Supervised model creation. To evaluate the scalability of the technique in real
scenarios, we used a robust, heterogeneous and far superior dataset than that used in the literature. Keywords: Editing programs detection | Machine learning processing | Multimedia container structure | Social networks detection | Video forensics | Video post-processing detection |
مقاله انگلیسی |
8 |
تشخیص چند نمایی چهره با استفاده از شبکه های عصبی عمیق
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 19 تشخیص چهره به طور گسترده در سیستم های هوشمندی مدرن مانند نظارت تصویری هوشمند، پرداخت آنلاین و سیستم دسترسی هوشمند مورد استفاده قرار گرفته است. الگوریتم های تشخیص چهره فعلی در معرض حمله انواع حملات ارائه چهره می باشند؛ کاغذ چاپ شده، بازپخش ویدئویی و ماسک های سیلیکونی از این جمله حملات اند. ما به منظور مدیریت بهینه مشکلات مذکور، معماری عمیق و جدیدی را صورت بندی نموده ایم که دقت تشخیص چندنمایی چهره انسان را افزایش می دهد. به ویژه، در وهله اول، شبکه عصبی عمیق و جدیدی به منظور رمزگذاری عمیق نواحی صورت ساخته شده است که در آن الگوریتم جدید تنظیم و تطبیق چهره به کار رفته است تا بر روی نقاط کلیدی موجود در چهره متمرکز گردد. بعد از آن، فناوری شناخته شده PCA را برای کم کردن ابعاد ویژگی های عمیق و به طور همزمان، حذف ویژگی های تصویری ناخالص و غیرضروری به کار برده ایم. سپس چارچوب اتصال بیزی را برای ارزیابی شباهت بردارهای ویژگی و دقت بسیار رقابتی دسته بندی چهره ها که می توان به آن دست یافت مطرح نمودیم. آزمایشات جامع بر روی مجموعه داده های کامپایل شده کاس-پیل انجام گرفته و عملکرد تشخیص چهره به میزان 98.52% موفقیت آمیز بود. علاوه بر این، سامانه پیشنهادی تشخیص چهره، به صورت سفت و سخت قادر به مدیریت حملات مختلف تشخیص چهره در زمینه های مختلف می باشد.
کلمات کلیدی: یادگیری عمیق | ناحیه صورت | تشخیص تصویر چهره | شبکه عصبی عمیق | کاهش ابعاد ویژگی PCA |
مقاله ترجمه شده |
9 |
AI Surveillance UGV
نظارت هوش مصنوعی UGV-2020 Security and surveillance are the prime focus for
any organization that chooses to be safe from any kind of
physical threats. Installation of video surveillance systems can
cost organizations a big portion of their financial budget and can
lead to significant changes in their network infrastructure. The
purpose of our research is to minimize the hurdles in setting up
the video surveillance system into organizations that has open
areas, that cannot afford to let any outsider to get into their
networks and that need many costly cameras to cover their
entire place. By installing cameras on an unmanned ground
vehicle (UGV) that can move around in a specified area using
geographic coordinate system, by smartly choosing its own path
for its automatically generated destinations within the area. The
UGV can also be manually controlled by the means of analog
transmission within specified range. The video is transmitted
using analog video transmitter and can be received over a
specific channel using the analog video receiver, and can be
observed using the developed software on any computer. Software
developed for the ground control station can smartly identify an
employee or an outsider using deep learning model trained on
the faces of the employees and can alert the organization on its
own when it detects any intrusion. Our research increased the
range of wireless video surveillance and reduced the financial
and architectural barriers for installing video surveillance into
the organization. Index Terms: wireless | security | surveillance | unmanned vehicle | image processing |
مقاله انگلیسی |
10 |
Early warning system: From face recognition by surveillance cameras to social media analysis to detecting suspicious people
سیستم هشدار اولیه: از تشخیص چهره توسط دوربین های نظارتی گرفته تا تحلیل رسانه های اجتماعی برای تشخیص افراد مشکوک-2020 Surveillance security cameras are increasingly deployed in almost every location for
monitoring purposes, including watching people and their actions for security purposes.
For criminology, images collected from these cameras are usually used after an incident
occurs to analyze who could be the people involved. While this usage of the cameras
is important for a post crime action, there exists the need for real time monitoring to
act as an early warning to prevent or avoid an incident before it occurs. In this paper,
we describe the development and implementation of an early warning system that
recognizes people automatically in a surveillance camera environment and then use data
from various sources to identify these people and build their profile and network. The
current literature is still missing a complete workflow from identifying people/criminals
from a video surveillance to building a criminal information extraction framework and
identifying those people and their interactions with others We train a feature extraction
model for face recognition using convolutional neural networks to get a good recognition
rate on the Chokepoint dataset collected using surveillance cameras. The system also
provides the function to record people appearance in a location, such that unknown
people passing through a scene excessive number of times (above a threshold decided
by a security expert) will then be further analyzed to collect information about them.
We implemented a queue based system to record people entrance. We try to avoid
missing relevant individuals passing through as in some cases it is not possible to add
every passing person to the queue which is maintained using some cache handling
techniques. We collect and analyze information about unknown people by comparing
their images from the cameras to a list of social media profiles collected from Facebook
and intelligent services archives. After locating the profile of a person, traditional news
and other social media platforms are crawled to collect and analyze more information
about the identified person. The analyzed information is then presented to the analyst
where a list of keywords and verb phrases are shown. We also construct the person’s
network from individuals mentioned with him/her in the text. Further analysis will allow
security experts to mark this person as a suspect or safe. This work shows that building
a complete early warning system is feasible to tackle and identify criminals so that
authorities can take the required actions on the spot. Keywords: Surveillance | Security camera | Monitoring | Early warning | Social media | Intelligence service |
مقاله انگلیسی |