Deep Learning Based Couple-like Cooperative Computing Method for IoT-based Intelligent Surveillance Systems

Security refers to the perceptions about an environment protection, it means without worry of suffer harm. This research offers a literature review about security subject, focused on autonomous surveillance, gathering in a single document the technical novelties about surveillance systems, their applications, and central components. During this research , we observe that deep learning its being applied for surveillance purpose, opening new research horizons, in an area which does not have been significant changes during about ten years, and we also found that new vast datasets are being produced to solve issues regarding security. We have also seen that, in terms of security, deep learning is highly viable to solve problems that have been implicit in security systems for a long time, this being able to turn deep learning into a new breakthrough with respect to systems programmed only by traditional vision algorithms, opening the possibility of becoming a mandatory accessory for security of systems. This research has been limited only on civil area surveillance systems, also we only use scientific articles for this, avoiding commercial technologies.

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Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Applied Sciences ◽

10.3390/app9224871 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4871 ◽

Cited By ~ 4

Author(s):

Quan Liu ◽

Chen Feng ◽

Zida Song ◽

Joseph Louis ◽

Jian Zhou

Keyword(s):

Deep Learning ◽

Model Comparison ◽

Surveillance Systems ◽

Comparison Study ◽

Learning Models ◽

The Core ◽

Dump Trucks ◽

Deep Learning Model ◽

Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

Journal of Imaging ◽

10.3390/jimaging7020012 ◽

2021 ◽

Vol 7 (2) ◽

pp. 12

Author(s):

Yousef I. Mohamad ◽

Samah S. Baraheem ◽

Tam V. Nguyen

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Data Augmentation ◽

Olympic Games ◽

Event Recognition ◽

Surveillance Systems ◽

Video Captioning ◽

Practical Applications ◽

Sport Events ◽

The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

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Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Software Practice and Experience ◽

10.1002/spe.2586 ◽

2018 ◽

Vol 48 (8) ◽

pp. 1475-1492 ◽

Cited By ~ 14

Author(s):

Rustem Dautov ◽

Salvatore Distefano ◽

Dario Bruneo ◽

Francesco Longo ◽

Giovanni Merlino ◽

...

Keyword(s):

Urban Areas ◽

Edge Computing ◽

Surveillance Systems ◽

Intelligent Surveillance

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Præventive potentialer – Populærkulturens overvejelser om overvågning

Slagmark - Tidsskrift for idéhistorie ◽

10.7146/sl.v0i58.104714 ◽

2018 ◽

pp. 99-112

Author(s):

Lone Koefoed Hansen ◽

Christopher Gad

Keyword(s):

Popular Culture ◽

Technological Development ◽

Entry Point ◽

Surveillance Systems ◽

Research Project ◽

Intelligent Surveillance ◽

Technological Research ◽

The Future ◽

Minority Report ◽

Basic Level

This article uses the movie Minority Report (2002) as an entry point for discussing conceptions of surveillance technologies and their preventive capacities. The technological research project Intelligent Surveillance Systems located in Belfast shares a vision with MR: that it is possible to construct surveillance systems that are able to foresee criminal acts and thus to prevent them from happening. We argue that the movie exemplifies that technological development and popular culture share dreams, ideas and visions and that on a very basic level, popular culture informs technological development and vice versa. The article explores this relation and argues that popular culture provides analytic insight on important discussions about surveillance and the (future) capacities of technology.

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A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sensors ◽

10.3390/s21227543 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7543

Author(s):

Bogdan Ilie Sighencea ◽

Rareș Ion Stanciu ◽

Cătălin Daniel Căleanu

Keyword(s):

Deep Learning ◽

Performance Metrics ◽

Evaluation Process ◽

Surveillance Systems ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Trajectory Prediction ◽

Processing Technologies ◽

Practical Applications ◽

New Research

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

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DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION

Kursor ◽

10.21107/kursor.v10i4.242 ◽

2020 ◽

Vol 10 (4) ◽

Author(s):

Basuki Rahmat ◽

Budi Nugroho

Keyword(s):

Deep Learning ◽

Internet Of Things ◽

Robot Control ◽

Learning Algorithm ◽

Control Program ◽

Robotic Control ◽

Intelligent Surveillance ◽

Deep Learning Algorithm ◽

Electronic Board ◽

Mobile Robot Control

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.

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