Real-Time Object Detection in Embedded Video Surveillance Systems

2008 ◽  
Author(s):  
Liliana Lo Presti ◽  
Marco La Cascia
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Video Surveillance ◽  
Surveillance Systems

Accelerating real‐time object detection in high‐resolution video surveillance

2021 ◽  
Author(s):  
Yuefeng Wang ◽  
Kuang Mao ◽  
Tong Chen ◽  
Yanglong Yin ◽  
Shuibing He ◽  
...  
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Video Surveillance

Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance Systems

2009 ◽  
Author(s):  
Michele Magno ◽  
Federico Tombari ◽  
Davide Brunelli ◽  
Luigi Di Stefano ◽  
Luca Benini
Keyword(s):  
Low Power ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance Systems

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.

Three-Pronged Compensation and Hysteresis Thresholding for Moving Object Detection in Real-Time Video Surveillance

2017 ◽  
Vol 64 (6) ◽  
pp. 4945-4955 ◽  
Author(s):  
Chia-Hung Yeh ◽  
Chih-Yang Lin ◽  
Kahlil Muchtar ◽  
Hsiang-Erh Lai ◽  
Ming-Ting Sun
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Video Surveillance ◽  
Moving Object Detection ◽  
Moving Object ◽  
Hysteresis Thresholding

A generic flexible and robust approach for intelligent real-time video-surveillance systems

2004 ◽  
Author(s):  
Xavier Desurmont ◽  
Jean-Francois Delaigle ◽  
Arnaud Bastide ◽  
Benoit Macq
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance Systems ◽  
Robust Approach
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