Driven by the significant advancements in technology and social issues such as security management, there is a strong need for Smart Surveillance System in our society today. One of the key features of a Smart Surveillance System is efficient human detection and counting such that the system can decide and label events on its own. In this paper we propose a new, novel and robust model, The Omega Model, for detecting and counting human beings present in the scene. The proposed model employs a set of four distinct descriptors for identifying the unique features of the head, neck and shoulder regions of a person. This unique head neck shoulder signature given by the Omega Model exploits the challenges such as inter person variations in size and shape of peoples head, neck and shoulder regions to achieve robust detection of human beings even under partial occlusion, dynamically changing background and varying illumination conditions. After experimentation we observe and analyze the influences of each of the four descriptors on the system performance and computation speed and conclude that a weight based decision making system produces the best results. Evaluation results on a number of images indicate the validation of our method in actual situation.
Sri Venkateswara College of Engineering（斯里文卡特斯瓦拉工程学院）
Over the last decade, there has been a spike in criminal activity all around the globe. According to the Indian police department, vehicle theft is one of the least solved offenses, and almost 19% of all recorded cases are related to motor vehicle theft. To overcome these adversaries, we propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed. The proposed system extracts various attributes of the vehicle such as Make, Model, Color, License plate number, and type of the license plate. Various image processing and deep learning alg…
Can we see it all? Do we know it All? These are questions thrown to human beings in our contemporary society to evaluate our tendency to solve problems. Recent studies have explored several models in object detection; however, most have failed to meet the demand for objectiveness and predictive accuracy, especially in developing and under-developed countries. Consequently, several global security threats have necessitated the development of efficient approaches to tackle these issues. This paper proposes an object detection model for cyber-physical systems known as Smart Surveillance Systems (…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of …
Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Ar…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewi…
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camera-based system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediatel…
Shenzhen Deepcam Information Technologies（深圳深目科技）；LinkSprite Technologies USA（美国领智科技）
Gun violence is a severe problem in the world, particularly in the United States. Deep learning methods have been studied to detect guns in surveillance video cameras or smart IP cameras and to send a real-time alert to security personals. One problem for the development of gun detection algorithms is the lack of large public datasets. In this work, we first publish a dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification we collect from a few different sources. To our knowledge, this is the largest dataset for the study of gun dete…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Action recognition is a key algorithmic part of emerging on-the-edge smart video surveillance and security systems. Skeleton-based action recognition is an attractive approach which, instead of using RGB pixel data, relies on human pose information to classify appropriate actions. However, existing algorithms often assume ideal conditions that are not representative of real-world limitations, such as noisy input, latency requirements, and edge resource constraints. To address the limitations of existing approaches, this paper presents Real-World Graph Convolution Networks (RW-GCNs), an archite…
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
Institute of Digital Media, Peking University（北京大学数字媒体研究所）；SECE of Shenzhen Graduate School, Peking University（北京大学深圳研究生院信息工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Lenovo Research（联想研究院）
Indian Institute of Information Technology, Nagpur（那格浦尔印度信息技术学院）
The current concept of Smart Cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and give a decent quality of life to its residents. To fulfill this need video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we surveyed various methodologies developed to detect anomalies in intelligent video surveillance. Firstly, we revisi…
University of Cincinnati（辛辛那提大学）；Central Michigan University（中央密歇根大学）；University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）
Action detection and public traffic safety are crucial aspects of a safe community and a better society. Monitoring traffic flows in a smart city using different surveillance cameras can play a significant role in recognizing accidents and alerting first responders. The utilization of action recognition (AR) in computer vision tasks has contributed towards high-precision applications in video surveillance, medical imaging, and digital signal processing. This paper presents an intensive review focusing on action recognition in accident detection and autonomous transportation systems for a smart…
Indian Institute of Information Technology, Nagpur（那格浦尔印度信息技术学院）；Visvesvaraya National Institute of Technology, Nagpur（维斯瓦拉亚国家理工学院那格浦尔分校）
Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes, we employed…
School of Engineering, Polytechnic of Porto (ISEP/IPP)（波尔图理工学院工程学院（ISEP/IPP））；Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD)（高级创新与发展智能工程与计算研究组（GECAD））
Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalone fire and intrusion detection solutions must be installed and maintained independently, which leads to high capital and operational costs. Nonetheless, due to recent developments in smart sensors, computer vision techniques, and wireless communication technologies, these solutions can be integrated in a modular and low-cost manner. This work introduces Integrated Physical Security System (IP2S), a multi-agent system…
University of Pisa（比萨大学）；National Research Council (ISTI-CNR)（国家研究委员会（信息科学与技术研究所））
Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer vision techniques rely on deep learning-based algorithms that estimate pedestrian densities in still, individual images. Only a bunch of works take advantage of temporal consistency in video sequences. In this work, we propose a spatio-temporal attentive neural network to estimate the number of pedestrians from surveillance videos. By taking advantage of the temporal correlation between consecutive frames, we lowere…
Video Forgery Detection for Surveillance Cameras: A Review
Kurdistan Technical Institute（库尔德斯坦技术学院）；University of Kurdistan Hewlêr（库尔德斯坦赫威尔大学）；University of Raparin（拉帕林大学）；Charmo University（恰尔莫大学）；Tishk International University（蒂什克国际大学）；Maharaja Sriram Chandra Bhanja Deo University（马哈拉贾·斯里拉姆·钱德拉·班贾·德奥大学）
Daffodil International University（达法迪尔国际大学）；University of Calgary（卡尔加里大学）
Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force s…
COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. It has resulted in an ongoing pandemic that caused infected cases including some deaths. Coronavirus is primarily spread between people during close contact. Motivating to this notion, this research proposes an artificial intelligence system for social distancing classification of persons by using thermal images. By exploiting YOLOv2 (you look at once), a deep learning detection technique is developed for detecting and tracking people in indoor and outdoor scenarios. An …
AdvSerial: Physical Adversarial Attacks on Infrastructure-mounted Pedestrian Detectors via Semantic Feature Suppression
School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院）；State Key Lab of Intelligent Transportation System（智能交通系统国家重点实验室）；Zhongguancun Laboratory（中关村实验室）
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学电气与计算机工程学院）；Transportation Research Institute (IMOB), UHasselt-Hasselt University（乌赫斯泰特大学交通研究所）；Faculty of Mechanical Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学机械工程学院）；Department of Computer Engineering, Amirkabir University of Technology（阿姆irkabir技术大学计算机工程系）
