scholarly journals Anomaly Detection in Banking Using A Video Surveillance System

2020 ◽  
pp. 538-545
Author(s):  
Saravanan S
Keyword(s):  
Face Recognition ◽  
Anomaly Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Region Of Interest ◽  
Video Surveillance System ◽  
Human Beings ◽  
Work Time ◽  
Detection Scheme ◽  
Video Sharing

An anomaly detection scheme is proposed for encrypted video bitstream with secure video encryption. Human beings are recognized by their unique facial characteristics. In the present work time based movement and face recognition approach will be implement to detect person in unwanted time.In video sharing , ROI (Region of Interest) extraction can be implement to detect the region to hide. An efficient encryption technique is used to encrypt the extracted region.

scholarly journals A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

Contextual Anomaly Detection Based Video Surveillance System

2021 ◽  
Author(s):  
Sawsen Abdulhadi Mahmood ◽  
Azal Monshed Abid ◽  
Wedad Abdul Khuder Naser
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System

A Novel Real-Time Fall Detection System Based on Real-Time Video and Mobile Phones

2016 ◽  
Vol 26 (04) ◽  
pp. 1750056
Author(s):  
Chao Tong ◽  
Yu Lian ◽  
Yang Zhang ◽  
Zhongyu Xie ◽  
Xiang Long ◽  
...  
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Mobile Phones ◽  
Surveillance System ◽  
Fall Detection ◽  
The Elderly ◽  
Video Surveillance System ◽  
Detection Scheme ◽  
Detection Systems ◽  
Time Fall

In recent years, due to the growing population of the elderly, falls of elderly people have aroused wide public concern. Detecting timely falls of the elderly is significant to their safety. Numerous challenges exist in real-time fall detection systems because some features of normal human activities are greatly similar to the characteristics of falls. To address these problems, we propose a novel fall detection scheme and build a health-care system to detect falls of the elderly based on a real-time video surveillance system and a smart phone. The system contains two major modules. The first module is a feature extraction module. We adopt the Gaussian mixture model, tracking learning detecting algorithm and logpolar histogram to extract the characteristics of falls from the video surveillance system and the sensors embedded in mobile phones. The main purpose of the second module is to detect a fall-based on the features obtained in the first module. The experimental results show that every module is significant. Besides, our system is effective to separate falls from other similar actions such as bend down with an accuracy rate of more than 98% and performs better than other state-of-the-art fall detection systems.

scholarly journals Real time face recognition of video surveillance system using haar cascade classifier

2021 ◽  
Vol 21 (3) ◽  
pp. 1389
Author(s):  
Adlan Hakim Ahmad ◽  
Sharifah Saon ◽  
Abd Kadir Mahamad ◽  
Cahyo Darujati ◽  
Sri Wiwoho Mudjanarko ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Video Surveillance System ◽  
Cascade Classifier ◽  
Camera System ◽  
Surveillance Camera ◽  
Haar Cascade

<div>This project investigates the use of face recognition for a surveillance system. The normal video surveillance system uses in closed-circuit television (CCTV) to record video for security purpose. It is used to identify the identity of a person through their appearances on the recorded video, manually. Today’s video surveillance camera system usually not occupied with a face recognition system. With some modification, a surveillance camera system can be used as face detection and recognition that can be done in real-time. The proposed system makes use of surveillance camera system that can identify the identity of a person automatically by using face recognition of Haar cascade classifier. The hardware used for this project were Raspberry Pi as a processor and Pi Camera as a camera module. The development of this project consist of three main phases which were data gathering, training recognizer, and face recognition process. All three phases have been executed using Python programming and OpenCV library, which have been performed in a Raspbian operation system. From the result, the proposed system successfully displays the output result of human face recognition, with facial angle within ±40°, in medium and normal light condition, and within a distance of 0.4 to 1.2 meter. Targeted image are allowed to wear face accessory as long as not covering the face structure. In conclusion, this system considered, can reduce the cost of manpower in order to identify the identity of a person in real time situation.</div>

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