scholarly journals Intelligent Video Surveillance System

2021 ◽  
Vol 9 (VIII) ◽  
pp. 741-743
Author(s):  
Dabbara Praveen
Keyword(s):  
Surveillance System ◽  
Identity Theft ◽  
Video Surveillance System ◽  
Security Monitoring ◽  
Processing Data ◽  
Intelligent Video Surveillance System ◽  
Unlabelled Data ◽  
Video Recognition ◽  
Learning Concept ◽  
Depth Learning

Intelligent video recognition with in-depth learning concept will create a self-paced video analytics program. CCTV cameras are used in all areas where safety is paramount. Manual monitoring seems tedious and time-consuming. Security can be defined by different words in different contexts such as identity theft, violence, explosions etc. Security monitoring is a tedious and time-consuming task. In this project we will analyse video feeds in real time and identify any unusual items such as violence or theft. The concept of in-depth learning simulates the functioning of the human brain in processing data for use in acquisition, speech recognition, decision making, etc. This will depend without human guidance, from unstructured and unlabelled data.

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.

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