Visual Surveillance Transformer

2021 ◽  
Vol 27 (12) ◽  
pp. 972-977
Author(s):  
Keong-Hun Choi ◽  
Jong-Eun Ha
Keyword(s):  
Visual Surveillance

Great Expectations: Perceptual Challenges of Visual Surveillance in Lifeguarding

2012 ◽  
Author(s):  
Lyndsey K. Lanagan-Leitzel ◽  
Emily Skow ◽  
Cathleen M. Moore
Keyword(s):  
Visual Surveillance ◽  
Great Expectations

A Fast Method for Defogging of Outdoor Visual Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Intelligent Vehicles ◽  
Visual Surveillance ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Data Sets ◽  
Fast Method ◽  
Time Data ◽  
Image Defogging ◽  
Clear Vision ◽  
Dark Channel

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.

scholarly journals Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average

2021 ◽  
Vol 11 (12) ◽  
pp. 5563
Author(s):  
Jinsol Ha ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Action Recognition ◽  
High Speed ◽  
Moving Average ◽  
Video Clip ◽  
Visual Surveillance ◽  
Temporal Information ◽  
Feature Maps ◽  
Short Term ◽  
Accurate Analysis ◽  
Difference Images

Action recognition requires the accurate analysis of action elements in the form of a video clip and a properly ordered sequence of the elements. To solve the two sub-problems, it is necessary to learn both spatio-temporal information and the temporal relationship between different action elements. Existing convolutional neural network (CNN)-based action recognition methods have focused on learning only spatial or temporal information without considering the temporal relation between action elements. In this paper, we create short-term pixel-difference images from the input video, and take the difference images as an input to a bidirectional exponential moving average sub-network to analyze the action elements and their temporal relations. The proposed method consists of: (i) generation of RGB and differential images, (ii) extraction of deep feature maps using an image classification sub-network, (iii) weight assignment to extracted feature maps using a bidirectional, exponential, moving average sub-network, and (iv) late fusion with a three-dimensional convolutional (C3D) sub-network to improve the accuracy of action recognition. Experimental results show that the proposed method achieves a higher performance level than existing baseline methods. In addition, the proposed action recognition network takes only 0.075 seconds per action class, which guarantees various high-speed or real-time applications, such as abnormal action classification, human–computer interaction, and intelligent visual surveillance.

scholarly journals Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance

2015 ◽  
Vol 10 (7) ◽  
pp. 1359-1370 ◽  
Author(s):  
Kevin Lin ◽  
Shen-Chi Chen ◽  
Chu-Song Chen ◽  
Daw-Tung Lin ◽  
Yi-Ping Hung
Keyword(s):  
Object Detection ◽  
Visual Surveillance ◽  
Temporal Consistency ◽  
Abandoned Object

DEWS: A Live Visual Surveillance System for Early Drowning Detection at Pool

2008 ◽  
Vol 18 (2) ◽  
pp. 196-210 ◽  
Author(s):  
How-Lung Eng ◽  
Kar-Ann Toh ◽  
Wei-Yun Yau ◽  
Junxian Wang
Keyword(s):  
Surveillance System ◽  
Visual Surveillance

Distributed intelligence for multi-camera visual surveillance

2004 ◽  
Vol 37 (4) ◽  
pp. 675-689 ◽  
Author(s):  
P. Remagnino ◽  
A.I. Shihab ◽  
G.A. Jones
Keyword(s):  
Visual Surveillance ◽  
Distributed Intelligence
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