scholarly journals Surveillance Video Based Robust Detection and Notification of Real Time Suspicious Activities in Indoor Scenarios

2016 ◽  
Author(s):  
Nithya Shree R ◽  
Rajeshwari Sah ◽  
Shreyank N Gowda
Keyword(s):  
Real Time ◽  
Surveillance Video ◽  
Robust Detection ◽  
Indoor Scenarios

scholarly journals Probe-Based Real-Time qPCR Assays for a Reliable Differentiation of Capripox Virus Species

2021 ◽  
Vol 9 (4) ◽  
pp. 765
Author(s):  
Janika Wolff ◽  
Martin Beer ◽  
Bernd Hoffmann
Keyword(s):  
Real Time ◽  
Animal Health ◽  
Diagnostic Methods ◽  
Virus Species ◽  
Time Saving ◽  
Robust Detection ◽  
Highly Sensitive ◽  
Reliable Differentiation ◽  
World Organization ◽  
Real Time Qpcr

Outbreaks of the three capripox virus species, namely lumpy skin disease virus, sheeppox virus, and goatpox virus, severely affect animal health and both national and international economies. Therefore, the World Organization for Animal Health (OIE) classified them as notifiable diseases. Until now, discrimination of capripox virus species was possible by using different conventional PCR protocols. However, more sophisticated probe-based real-time qPCR systems addressing this issue are, to our knowledge, still missing. In the present study, we developed several duplex qPCR assays consisting of different types of fluorescence-labelled probes that are highly sensitive and show a high analytical specificity. Finally, our assays were combined with already published diagnostic methods to a diagnostic workflow that enables time-saving, reliable, and robust detection, differentiation, and characterization of capripox virus isolates.

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

scholarly journals Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchain

2018 ◽  
Author(s):  
Seyed Yahya Nikouei ◽  
Ronghua Xu ◽  
Deeraj Nagothu ◽  
Yu Chen ◽  
Alexander Aved ◽  
...  
Keyword(s):  
Real Time ◽  
Surveillance Video ◽  
Time Index

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.

Automatic Helmet Detection in Real-Time and Surveillance Video

2020 ◽  
pp. 51-60
Author(s):  
Shubham Kumar ◽  
Nisha Neware ◽  
Atul Jain ◽  
Debabrata Swain ◽  
Puran Singh
Keyword(s):  
Real Time ◽  
Surveillance Video
Sign in / Sign up

Export Citation Format

Share Document