Turnstile Jumping Detection in Real-Time Video Surveillance

2019 ◽  
pp. 390-403
Author(s):  
Huy Hoang Nguyen ◽  
Thi Nhung Ta
Keyword(s):  
Real Time ◽  
Video Surveillance

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

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

scholarly journals Remote Video Surveillance System Based on Direct Show and Image Compression

2013 ◽  
Vol 756-759 ◽  
pp. 439-442
Author(s):  
Shang Fu Gong ◽  
Li Gang Wu ◽  
Yan Jun Wang
Keyword(s):  
Image Compression ◽  
Real Time ◽  
Video Surveillance ◽  
Data Transmission ◽  
Video Transmission ◽  
Massive Data ◽  
Time Requirements ◽  
Rich Information ◽  
Precautionary Measures ◽  
Application Requirements

Video surveillance, convenience and rich information, has been widely used in security, protection, monitoring and other occasions, and has already been one of the most important precautionary measures in commercial, residential and transportation areas. However, considering the massive data transmission needs and higher real-time requirements for video surveillance, a remote video surveillance plan has been put forward on the analysis basis of Microsoft DirectShow and Image Compression. This plan adopts the C/S structure, adapts to the requirements of real-time video transmission, with a better fluency. In addition, the picture clarity satisfies the application requirements.

Edge Artificial Intelligence: A Multi-Camera Video Surveillance Application

2021 ◽  
Author(s):  
Daniele Berardini ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Sara Moccia
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Surveillance ◽  
Cost Effective ◽  
Fast Response ◽  
Video Stream ◽  
Single Shot ◽  
Central Unit ◽  
Surveillance Video ◽  
People Detection

Abstract Nowadays, video surveillance has a crucial role. Analyzing surveillance videos is, however, a time consuming and tiresome procedure. In the last years, artificial intelligence paved the way for automatic and accurate surveillance-video analysis. In parallel to the development of artificial-intelligence methodologies, edge computing is becoming an active field of research with the final goal to provide cost-effective and real time deployment of the developed methodologies. In this work, we present an edge artificial intelligence application to video surveillance. Our approach relies on a set of four IP cameras, which acquire video frames that are processed on the edge using the NVIDIA® Jetson Nano. A state-of-the-art deep-learning model, called Single Shot multibox Detector (SSD) MobileNetV2 network, is used to perform object and people detection in real-time. The proposed infrastructure obtained an inference speed of ∼10.0 Frames per Second (FPS) for each parallel video stream. These results prompt the possibility of translating our work into a real word scenario. The integration of the presented application into a wider monitoring system with a central unit could bring benefits to the overall infrastructure. Indeed our application could send only video-related high-level information to the central unit, allowing it to combine information with data coming from other sensing devices without unuseful data overload. This would ensure a fast response in case of emergency or detected anomalies. We hope this work will contribute to stimulate the research in the field of edge artificial intelligence for video surveillance.

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