Fatimah Khodadin

Bio: Fatimah Khodadin is an academic researcher. The author has contributed to research in topics: Smart camera. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

An Intelligent Camera Surveillance System with Effective Notification Features

Abstract: The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems. Other than regular functionalities such as motion detection, object detection, face recognition and counting people, it also integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder. When people are detected, a counting module displays the number of persons present in the surveillance area. A face recognition module distinguishes between authorised and unauthorised users. This biometric functionality reduces false alarms which makes the system more robust. An object detection module detects certain valuable objects such as handbags, laptops and smartphones. Also, images and short video recordings are stored on the cloud. Furthermore, the system introduces innovative real-time notification approaches for surveillance systems such as WhatsApp messages and phone calls, in addition to SMS and emails. Thus, this system is reliable and meets the aim of a modern intelligent surveillance system by combining multiple approaches to detect intrusions and to inform users effectively.

A Review of Blockchain for Security Data Privacy with Metaverse

Abstract: Metaverse is a world that is being developed now with the metaverse, the virtual world will develop rapidly because it creates a separate world, the metaverse relationship with the blockchain is created because there is a distribution of data with the blockchain method, the distribution of data will be better because the data will be sent securely using the data privacy process. The method used in this research is to use the literature review method and literature study, with the previous literature, it can be ascertained that this research is the latest research, because it has novelty based on previous research, therefore this research will be strong because it can find problems previous research that has been done. The intent and purpose of this research is how the metaverse is linked to the blockchain method, by paying attention to data security in privacy, with this concept, the distribution of data will be safer because it uses the blockchain method. In this research, how to come up with a system and try several aspects that exist in metaforce and blocking to produce a measurable impact.

Smart Surveillance Vigilant Detection and Notification System

Abstract: Surveillance system plays a key role in maintaining the security in today's life. But, fail to provide the feature of avoiding the unfortunate happenings. This work comes up with an idea of smart surveillance system designed such a way that the user is notified when upcoming dangers are detected. In this work, motion of the intruders and the presence of a human faces are detected using image processing algorithms. As the motion detected in the locality of the surveillance area, a short video clip is recorded and sent to the user along with an alert message through the email server controlled by Raspberry Pi. When the cloud is not available, the data is stored locally in the Raspberry Pi and is sent to the user when the connection restarts. Live streaming video from the camera is accessed on any Internet enabled device. Surveillance camera rotates with the help of a DC motor. Also, temperature and gas sensors are integrated with the system.

Strategies and Tools for Effective Suspicious Event Detection from Video: A Survey Perspective (COVID-19)

Abstract: Safety and health security are the major concerns in today's modern world. Most of the countries have adopted camera surveillance systems to achieve a secure environment, one such example is restriction imposed for the movement of people during the COVID-19 pandemic. Thus, surveillance systems serve the purpose of humans to identify intruders with suspicious behavior. Detecting these intrusions or any suspicious events in an early stage from surveillance systems is an important and challenging task. This can be done using Suspicious Event Detection Models (SEDM) and tools. In earlier systems, it is found that machine learning methods are proved to be efficient in predicting the suspicion activities. In this chapter, a survey of various SEDM Strategies and Tools that were developed earlier which captures the suspicious events in campuses or societies is discussed that provides a Full-proof secure environment. Few, earlier SEDM have also used deep learning approaches, IoT, and fuzzy logic techniques. Finally, an improved SEDM for campuses based on deep learning is suggested. The capability of deep learning (CNN) method is very influential in extraction of features from unstructured contents, especially from captured images of video. The efficiency of this suggested SEDM will be better when compared with earlier state-of-art-systems, which do not support alarming system by making use of GPS, and then extracting personal details.