High-level surveillance event detection

2017 ◽  
Vol 01 (01) ◽  
pp. 1630007
Author(s):  
Fabio Persia ◽  
Daniela D’Auria
Keyword(s):  
Video Surveillance ◽  
Event Detection ◽  
Terrorist Attacks ◽  
Surveillance Systems ◽  
Detection Systems ◽  
Whole Process ◽  
Video Frames ◽  
The World ◽  
Public Areas ◽  
High Level

Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Promenade des Anglais in Nice. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this work, we present an overview of the evolution of high-level surveillance event detection systems along with a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied.

scholarly journals Damaged Number Plate Recognition

2019 ◽  
Vol 9 (1S) ◽  
pp. 22-25
Keyword(s):  
Video Surveillance ◽  
Input Image ◽  
Surveillance Systems ◽  
Additional Error ◽  
Entire Process ◽  
Plate Number ◽  
The World ◽  
Automated Video Surveillance

Around the world every vehicle are identified by its number plate. Number plate detection is one of the existing automated video surveillance systems that are used to detect the number plate. This system fails if the number plates are damaged, no proper illumination, blurry images. Thus here we will be able to recognizeze such damaged number plate. The technique involves four main stages viz. pre-processing, localization, recognition and segmentation. The entire process includes capturing the image, erasing the background details and removing the noise, cropping the number plate and then recognizing the characters followed by segmenting in order to recognize the plate. All this is done in Python because it had better results compared to MATLAB. When done in MATLAB, additional error and noise gets added to the input image and can causes inclusion of a new characters in the number plate and leads to misinterpretation of the number plate. About 100 images were gathered and 98 images of them were detected correctly. The efficiency in recognizing the damaged number plate using our system is about 98%.

scholarly journals Interesting Video Frames Capturing on Digital Video Development Platform

2011 ◽  
pp. 17-20
Author(s):  
D. Nethaji ◽  
Mary Joans ◽  
Mrs. S. J. Grace Shoba
Keyword(s):  
Video Surveillance ◽  
Field Of View ◽  
Surveillance Systems ◽  
Development Platform ◽  
Video Images ◽  
Video Frames ◽  
Data Rates ◽  
Video Processor ◽  
Human Monitoring

Video surveillance has been a popular security tool for years. Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and in-effective. This paper presents a novel solution for real-time cases that identify and record only “interesting” video frames containing motion. In addition to traditional methods for compressing individual video images, we could identify and record only “interesting” video images, such as those images with significant amounts of motion in the field of view. The model would be built in simulink, one of tools in matlab and incorporated with davinci code processor, a video processor. That could significantly help reduce the data rates for surveillance-specific applications.

A Survey of Automatic Event Detection in Multi-Camera Third Generation Surveillance Systems

2015 ◽  
Vol 29 (01) ◽  
pp. 1555001 ◽  
Author(s):  
Tiziana D'Orazio ◽  
Cataldo Guaragnella
Keyword(s):  
Event Detection ◽  
Traffic Control ◽  
Urban Traffic ◽  
Semantic Interpretation ◽  
Surveillance Systems ◽  
Third Generation ◽  
Open Problems ◽  
Public Areas ◽  
Automatic Event Detection ◽  
Generation Surveillance

Third generation surveillance systems are largely requested for intelligent surveillance of different scenarios such as public areas, urban traffic control, smart homes and so on. They are based on multiple cameras and processing modules that integrate data coming from a large surveillance space. The semantic interpretation of data from a multi-view context is a challenging task and requires the development of image processing methodologies that could support applications in extensive and real-time contexts. This paper presents a survey of automatic event detection functionalities that have been developed for third generation surveillance systems with a particular emphasis on open problems that limit the application of computer vision methodologies to commercial multi-camera systems.

scholarly journals Real-time marine vessel surveillance video system

2021 ◽  
pp. 98-105
Author(s):  
Ш.С. Фахми ◽  
С.А. Селиверстов ◽  
Е.В. Костикова ◽  
Р.Р. Муксимова ◽  
В.О. Титов
Keyword(s):  
Video Surveillance ◽  
Video Processing ◽  
Image Sensor ◽  
Surveillance Systems ◽  
Processing Unit ◽  
Desktop Computer ◽  
Smart Cameras ◽  
Video Information ◽  
Observation Systems ◽  
High Level

Анализируется процесс развития систем наблюдения. Раскрываются особенности технологических изменений систем наблюдения 1-го, 2-го и 3-го поколений. Декларируется, что современные полупроводниковые технологии позволяют перейти к более развитым системам видеонаблюдения 3-го поколения, где преобразование и обработка видеоинформации выполняются непосредственно в видеодатчиках на этапе формирования кадров. Умные камеры расширяют функциональность видеосенсора 3-го поколения, обеспечивая бортовую высокоуровневую обработку видео. Рассмотрены эволюция систем наблюдения и архитектура обработки видеоинформации с использованием интеллектуальных видеокамер с высоким динамическим диапазоном. Представлена графическая интерпретация, иллюстрирующая процесс эволюции систем видеонаблюдения от 1-го к 3-му поколению. Проанализированы функции современных систем видеонаблюдения и переход от высокоуровневой обработки видео из систем общего назначения во встраиваемые системы. Рассмотрен состав видеосистемы наблюдения с использованием интеллектуальной видеокамеры, включающий видеодатчик, блок обработки и блок управления связи. Описаны условия в которых морские системы видеонаблюдения используются. Приведены результаты экспериментальных исследований и выполнены оценки производительности. Показаны достигнутые результаты производительности для различных реализаций алгоритма обнаружения морских судов и необходимое время выполнения при обработке одного изображения с полным разрешением на стандартном настольном компьютере Pentium 4 с частотой 2,4 ГГц. с использованием реконфигурируемой системой на кристалле. The process of development of observation systems is analyzed. The features of technological changes in observation systems of the 1st, 2nd and 3rd generations are revealed. It is declared that modern semiconductor technologies make it possible to move to more advanced third-generation video surveillance systems, where the conversion and processing of video information is performed directly in video sensors at the stage of framing. Smart cameras extend the functionality of the 3rd generation image sensor to provide on-board high-level video processing. The evolution of surveillance systems and architecture of video information processing using smart cameras with a high dynamic range are considered. A graphical interpretation is presented that illustrates the evolution of video surveillance systems from the 1st to the 3rd generation. The functions of modern video surveillance systems and the transition from high-level video processing from general-purpose systems to embedded systems are analyzed. The composition of a video surveillance system using an intelligent camera is considered, including a video sensor, a processing unit and a communication control unit. The conditions in which marine video surveillance systems are used are described. The results of experimental studies are presented and performance estimates are performed. Shown are the achieved performance results for various implementations of the ship detection algorithm and the required execution time when processing one full resolution image on a standard Pentium 4 desktop computer running at 2.4 GHz. using a reconfigurable system on a chip.

Intelligent Anomaly Detection Video Surveillance Systems for Smart Cities

2019 ◽  
pp. 163-182
Author(s):  
Gajendra Singh ◽  
Rajeev Kapoor ◽  
Arun K. Khosla
Keyword(s):  
Video Surveillance ◽  
Smart Cities ◽  
Vital Role ◽  
Personal Life ◽  
Monitoring Systems ◽  
Human Observer ◽  
Surveillance Systems ◽  
Public Areas ◽  
Long Time ◽  
Control And Management

With the growing demands of safety for people and their properties, video surveillance has drawn much attention. These requirements have led to the positioning of cameras almost every corner. Smart video surveillance systems can interpret the situation and automatically recognize abnormal situations, which plays a vital role in intelligence monitoring systems. One vital aspect is to detect and alert generation of suspicious events then to notify operators or users automatically. A long time may pass before an event of interest to take place. In such situations, human attention may get diverted and an event of interest may get missed. In such case, video surveillance systems can effectively improve safety and security for the control and management of public areas or personal life. Independent surveillance systems to replace the traditional (human observer-oriented) systems also can relieve the workload of relative personnel.

scholarly journals A Robust Watermarking Method for an Authentication of Video Surveillance Applications

2019 ◽  
Vol 9 (1S3) ◽  
pp. 500-506
Keyword(s):  
Video Surveillance ◽  
Wavelet Coefficient ◽  
Research Work ◽  
Digital Watermark ◽  
Video Watermarking ◽  
Discrete Wavelet ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Video Frames ◽  
Surveillance Applications

The problem of authenticating the video content is the major role of any automated video surveillance systems (AVS). This research work delivers an approach to authenticate the surveillance video which can be used by attorneys to prove their clients using Digital Watermarking. The digital watermark gives an assurance that the provided video surveillance frame is not tampered. In this paper, we proposed a robust video watermarking approach for an authentication of video surveillance applications. The digital watermark is embedded into the Discrete Wavelet domain of the identified key frames using holoentropy. Here, the pixel map will be optimallygenerated for the watermarking and the image embedding using the proposed classifier using Moth – flame - Rider optimization based Neural Network (MF-ROA-based NN) will generate the optimal map prediction based on the fitness measure, such as wavelet coefficient, energy, entropy, loop coefficient, and standard deviation, respectively. This optimal map is used for both embedding and extraction. The experimental results proves that the proposed systems can authenticate the surveillance video frames against various attacks when compared to the existing systems.

scholarly journals A Novel Framework for Anomaly Detection in Video Surveillance using Convolutional LSTM

2020 ◽  
Vol 9 (4) ◽  
pp. 355-359
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Event Detection ◽  
Video Data ◽  
Surveillance Systems ◽  
Abnormal Event Detection ◽  
Suggested Approach ◽  
Processing Technologies ◽  
Safety Requirements ◽  
Convolutional Lstm

Today, due to public safety requirements, surveillance systems have gained increased attention. Video data processing technologies such as the identification of activity [1], object tracking [2], crowd counting [3], and the detection of anomalies [ 4] have therefore been rapidly developing. In this study, we establish an unattended method for the detection of anomaly events in videos based on a ConvLSTM encoder-decoder to learn about the evolution of spatial characteristics. Our model only covers typical video events during preparation, whereas in testing the videos are both usual and abnormal. Experiments on the UCSD datasets confirm the validity of the suggested approach to abnormal event detection.

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