Low-Cost Camera-Based Smart Surveillance System for Detecting, Recognizing, and Tracking Masked Human Face

A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.

Remote Fault-Tolerant Control for Industrial Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

Weapon Detection Using YOLO V3 for Smart Surveillance System

Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

EyeBlink - “Smart Surveillance System for Detecting Interpersonal Crime”

Due to exponential increase in crime rate, surveillance systems are being put up in malls, stations, schools, airports etc. With the videos being captured 24x7 from these cameras, it is difficult to manually monitor them to detect suspicious objects. So, there is a great demand for intelligent surveillance system. The proposed work automatically detects multiple anomalous objects in videos. Due to exponential increase in crime rate. Surveillance systems are being put up in malls, stations, schools, Airports etc. With the videos being captured 24x7 from these cameras, it is difficult to manually monitor them to detect suspicious activities. So, there is a great demand for intelligent surveillance system. The proposed work automatically detects multiple anomalous activities in videos.

Detection of Moving Vehicles on Highway using Fuzzy Logic for Smart Surveillance System

The computer vision incorporates a prominent performance in developing models for medical, security and lot more concepts and we need to deal with detecting and tracking the moving objectsuch as moving vehicle or person. Various challenges are available due to environmental issues, illumination variation, or fast motion etc. This paper work has developed a fuzzy logic based method for identifying the moving vehicles though bounding boxas depicted in the green colour. The paper depictsa fuzzy based method to detect and track the object around the location until the specific secured dimension of the device. The extended recognition (EIR) is the proposed method which works on the automatic fuzzy set creation.The EIR consists of the pixels of the surroundings and recognize them with the predefined inputs we have with the EIR algorithm in the repository. This methodology successfully identified and detects vehicles. It can track the instances within that visible region and this is working as a human eye mechanism.