scholarly journals Artificial Intelligence in Video Surveillance

2020 ◽  
Vol 61 (2) ◽  
pp. 12-23
Author(s):  
Victoriia Buran ◽  
Keyword(s):  
Artificial Intelligence ◽  
Video Surveillance

Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

2021 ◽  
Author(s):  
Daniele Berardini ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Sara Moccia
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Surveillance ◽  
Cost Effective ◽  
Fast Response ◽  
Video Stream ◽  
Single Shot ◽  
Central Unit ◽  
Surveillance Video ◽  
People Detection

Abstract Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.

scholarly journals Research on Video Quality Diagnosis Technology Based on Artificial Intelligence and Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhidong Sun ◽  
Jie Sun ◽  
Xueqing Li
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Quality ◽  
Noise Detection ◽  
Diagnosis System ◽  
Human Eyes ◽  
Quality Diagnosis ◽  
The Internet Of Things

The remote video diagnosis system based on the Internet of Things is based on the Internet of Things and integrates advanced intelligent technology. To better promote a harmonious society, constructing a video surveillance system is accelerating in our country. Many enterprises and government agencies have invested much money to build video surveillance systems. The quality of video images is an important index to evaluate the video surveillance system. However, as the number of cameras continues to increase, the monitoring time continues to extend. In the face of many cameras, it is not realistic to rely on human eyes to diagnose video-solely quality. Besides, due to human eyes’ subjectivity, there will be some deviation in diagnosis through human eyes, and these factors bring new challenges to system maintenance. Therefore, relying on artificial intelligence technology and digital image processing technology, the intelligent diagnosis system of monitoring video quality is born using the computer’s efficient mathematical operation ability. Based on artificial intelligence, this paper focuses on studying video quality diagnosis technology and establishes a video quality diagnosis system for video definition detection and noise detection. This article takes the artificial intelligence algorithm in the diagnosis of video quality effect. Compared with the improved algorithm, the improved video quality diagnosis algorithm has excellent improvement and can well finish video quality inspection work. The accuracy of the improved definition evaluation function for the definition detection of surveillance video and noise detection is as high as 95.56%.

A New Human Intruder Detection Scheme for Video Surveillance

2019 ◽  
Author(s):  
Shaomin Xiong ◽  
Haoyu Wu ◽  
Toshiki Hirano
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Video Surveillance ◽  
Human Detection ◽  
Detection Scheme ◽  
Surveillance Camera ◽  
The Neural Network ◽  
Higher Power ◽  
Machine Leaning ◽  
Tracking And Recognition

Abstract The demand for video surveillance has increased rapidly in recent years. Artificial intelligence (AI) algorithms are key enablers for the smart functionalities of a surveillance camera. Typical smart functionalities include human or object detection, tracking and recognition. However, many of the neural network (NN) algorithms for AI require intensive computation. At the endpoint or edge such as a home surveillance camera, the computation power is limited. The intensive computation also causes higher power consumption, which is also problematic for battery powered cameras. In this paper, we introduce a new human detection scheme that requires much less computation while the accuracy is equivalent to other existing algorithms. It obtains datasets and knowledge from a complex NN algorithm at the learning and calibration phase. These datasets are later used to train two cascading lightweight machine leaning algorithms, which will be used for further human detections. It is demonstrated that the proposed scheme can be run by the camera alone and the speed of detection is much faster than other benchmark NN algorithms.

Human Action Pattern Recognition and Semantic Research Based on Embodied Cognition Theory

2019 ◽  
Vol 34 (05) ◽  
pp. 2055013 ◽  
Author(s):  
Liang Tan
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Video Surveillance ◽  
Human Behavior ◽  
Human Body ◽  
Body Motion ◽  
Behavior Recognition ◽  
Video Images ◽  
Human Behavior Recognition ◽  
Significant Region

Human body motion pattern recognition in video images is an important research direction in the field of pattern recognition. It has a very broad application prospect in many fields such as intelligent video surveillance, human-computer interaction, motion analysis, video retrieval, etc. Research has also received extensive attention from scholars at home and abroad. Pattern recognition is essentially a branch of artificial intelligence. It has its unique role in the field of artificial intelligence. Accurate recognition of human body motion patterns in video images is of great help in image classification, retrieval, human tracking and video surveillance. Based on the human visual perception mechanism, this paper proposes a human behavior recognition algorithm based on semantic saliency map. Through the combination of sliding window and similarity measure, the behavioral region that best exhibits the semantic features of the image is found, which is the semantically significant region. The semantic significant region and the original image are used as the dual input source to study the human behavior recognition, and the image is enhanced. The utilization of significant regional information better reveals the identifiable area of the image and contributes to the recognition of human behavior.

scholarly journals Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems

2021 ◽  
Vol 2089 (1) ◽  
pp. 012079
Author(s):  
Makkena Brahmaiah ◽  
Srinivasa Rao Madala ◽  
Ch Mastan Chowdary
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Deep Learning ◽  
Video Surveillance ◽  
Real World ◽  
Crime Rates ◽  
Surveillance Systems ◽  
Security Systems ◽  
Trade Off ◽  
Wide Range

Abstract As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.

scholarly journals Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nehemia Sugianto ◽  
Dian Tjondronegoro ◽  
Rosemary Stockdale ◽  
Elizabeth Irenne Yuwono
Keyword(s):  
Artificial Intelligence ◽  
Video Surveillance ◽  
Public Spaces ◽  
Privacy Preserving ◽  
Robust Performance ◽  
Implementation Framework ◽  
Content Type ◽  
Data Movement ◽  
Social Distancing

PurposeThe paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.Design/methodology/approachThe paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.FindingsThe proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.Originality/valueThe paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

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