WATER LEVEL FLUCTUATION USING SURVEILLANCE CAMERA

Floods are the most frequent type of natural disaster that cause loss of life and damages to personal property and eventually affect the economic state of the country. Researchers around the world have been made significant efforts in dealing with the flood issue. Computer vision is one of the common approaches being employed which include the use of image segmentation techniques for image understanding and image analysis. The technique has been used in various fields including in flood disaster applications. This paper explores the use of a hybrid segmentation technique in detecting water regions from surveillance images and introduces a flood index calculation to study water level fluctuations. The flood index was evaluated by comparing the result with water level measured by sensor on-site. The experimental results demonstrated that the flood index reflects the trend of water levels of the river. Thus, the proposed technique can be used in detecting water regions and monitoring the water level fluctuation of the river.

Design and kinematic analysis of new multi-mode mobile parallel mechanism with deployable platform

Purpose This study aims to make the mobile robot better adapt to the patrol and monitoring in industrial field substation area, a multi-mode mobile carrying mechanism which can carrying data collector, camera and other equipment is designed. Design/methodology/approach Based on the geometric axis analysis and interference analysis, the multi-mode mobile carrying mechanism is designed. The screw constraint topological theory and zero-moment point (ZMP) theory is used to kinematic analysis in mechanism mobile process. Findings The mobile carrying mechanism can realize the folding movement, hexagonal rolling and quadrilateral rolling movement. A series of simulation and prototype experiment results verify the feasibility and actual error of the design analysis. Originality/value The work of this paper provides a mobile carrying mechanism for carrying different data acquisition equipment and surveillance camera in industrial field substation zone. It has excellent folding performance and mobile capabilities. The mobile carrying mechanism reduces the workload of human being and injuries suffered by workers in industrial substation area.

Algorithmic Analysis of Automatic Attendance System using Facial Recognition

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.

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.

Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

SPDNet: A Real-Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results.

Social Distance Analysing - COVID-19 Using Deep Learning and Computer Vision

In the combat in opposition to the coronavirus, social distancing has tested to be an effective degree to bog down the unfold of the disease. The machine provided is for reading social distancing through calculating the space among humans for you to gradual down the unfold of the virus. This machine makes use of enter from video frames to parent out the space among people to relieve the impact of this pandemic. This is performed through comparing a video feed acquired through a surveillance camera. The video is calibrated into bird’s view and fed as an enter to the YOLOv3 version that is an already educated item detection version. The YOLOv3 version is educated using the Common Object in Context (COCO). The proposed machine turned into corroborated on a pre-filmed video. The outcomes and consequences acquired through the machine display that assessment of the space among more than one people and figuring out if policies are violated or not. If the space is less than the minimal threshold value, the people are represented through a purple bounding box, if not then it's far represented through a inexperienced bounding box. This machine may be similarly advanced to detect social distancing in real-time applications.

Development of surveillance drone based internet of things (IoT) for industrial security applications

Drones or mini-unmanned aerial vehicles, have becoming an emerging trends due to their boundless applications in surveillance, military and numerous public services. Nowadays, deployment of surveillance drone for monitoring or security application remains challenging and ongoing research. As Internet of Things (IoT) becomes more commercialized, various concept of IoT have been integrated with the drones due to efficient usage. Therefore, this paper proposed the development of surveillance drone system based on IoT for industrial monitoring-security applications. The rationale of integrating IoT with surveillance drone is that it allows authenticated users to login from any device, anywhere, and view video or images from surveillance drones in real-time for security awareness. In this work, the surveillance drone consists of mechanical system, electrical and electronic interfacing and IoT platform (mobile application system). In electronic system, power module, communication module, sensor and actuator as well as user interface module have been adopted and integrated into the systems. Besides, in software development system, user interface configuration was developed through mobile application to serve as IoT platform. A series of experiments shows that the surveillance drone based IoT able to operate with a promising flying distance with surveillance camera as the “eyes” of the drone system.