Biblio

Conventional Security Systems are improving with the advancement of Internet of Things (IoT) based technology. For better security, in addition to the currently available technology, surveillance systems are used. In this research, a Smart Surveillance System with machine-learning capabilities is designed to detect security breaches and it will resolve safety concerns. Machine learning algorithms are implemented to detect intruders as well as suspicious activities. Enery efficiency is the major concern for constant monitoring systems. As a result, the designed system focuses on power consumption by calibrating the system so that it can work on bare minimum power and additionally provides the required output. Fire sensor has also been integrated to detect fire for safety purposes. By adding upon the security infrastructure, next-generation smart surveillance systems can be created for a safe future. The developed system contains the necessary tools to recognize intruders by face recognition. Also using the ambient sensors (PIR sensor, fire detecting sensor), a secure environment is provided during working and non-working hours. The system shows high accuracy in human & flame detection. A more reliable security system can be created with the further development of this research.

In this work, we have proposed a state-of-the-art face logging system that detects and logs high quality cropped face images of the people in real-time for security applications. Multiple strategies based on resolution, velocity and symmetry of faces have been applied to obtain best quality face images. The proposed system handles the issue of motion blur in the face images by determining the velocities of the detections. The output of the system is the face database, where four faces for each detected person are stored along with the time stamp and ID number tagged to it. The facial features are extracted by our system, which are used to search the person-of-interest instantly. The proposed system has been implemented in a docker container environment on two edge devices: the powerful NVIDIA Jetson TX2 and the cheaper NVIDIA Jetson N ano. The light and fast face detector (LFFD) used for detection, and ResN et50 used for facial feature extraction are optimized using TensorRT over these edge devices. In our experiments, the proposed system achieves the True Acceptance Rate (TAR) of 0.94 at False Acceptance Rate (FAR) of 0.01 while detecting the faces at 20–30 FPS on NVIDIA Jetson TX2 and about 8–10 FPS on NVIDIA Jetson N ano device. The advantage of our system is that it is easily deployable at multiple locations and also scalable based on application requirement. Thus it provides a realistic solution to face logging application as the query or suspect can be searched instantly, which may not only help in investigation of incidents but also in prevention of untoward incidents.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

Witnessing the increasingly pervasive deployment of security video surveillance systems(VSS), more and more individuals have become concerned with the issues of privacy violations. While the majority of the public have a favorable view of surveillance in terms of crime deterrence, individuals do not accept the invasive monitoring of their private life. To date, however, there is not a lightweight and secure privacy-preserving solution for video surveillance systems. The recent success of blockchain (BC) technologies and their applications in the Internet of Things (IoT) shed a light on this challenging issue. In this paper, we propose a Lightweight, Blockchain-based Privacy protection (Lib-Pri) scheme for surveillance cameras at the edge. It enables the VSS to perform surveillance without compromising the privacy of people captured in the videos. The Lib-Pri system transforms the deployed VSS into a system that functions as a federated blockchain network capable of carrying out integrity checking, blurring keys management, feature sharing, and video access sanctioning. The policy-based enforcement of privacy measures is carried out at the edge devices for real-time video analytics without cluttering the network.

Deep learning is the segment of artificial intelligence which is involved with imitating the learning approach that human beings utilize to get some different types of knowledge. Analyzing videos, a part of deep learning is one of the most basic problems of computer vision and multi-media content analysis for at least 20 years. The job is very challenging as the video contains a lot of information with large differences and difficulties. Human supervision is still required in all surveillance systems. New advancement in computer vision which are observed as an important trend in video surveillance leads to dramatic efficiency gains. We propose a CCTV based theft detection along with tracking of thieves. We use image processing to detect theft and motion of thieves in CCTV footage, without the use of sensors. This system concentrates on object detection. The security personnel can be notified about the suspicious individual committing burglary using Real-time analysis of the movement of any human from CCTV footage and thus gives a chance to avert the same.