Research on foreground target extraction and image processing of surveillance video

2021 ◽  
Author(s):  
Zonghuang Xu
Keyword(s):  
Image Processing ◽  
Surveillance Video ◽  
Target Extraction

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

scholarly journals Recognition and Analysis of Behavior Features of School-Age Children Based on Video Image Processing

2020 ◽  
Vol 37 (4) ◽  
pp. 603-610
Author(s):  
Zhen Wang
Keyword(s):  
Image Processing ◽  
Optical Flow ◽  
Network Flow ◽  
Video Image ◽  
School Age Children ◽  
School Age ◽  
Surveillance Video ◽  
Flow Network ◽  
Time Flow ◽  
Video Image Processing

School-age children have vastly different behavior features from adults. Most of the relevant studies are theoretical summaries of behavior features of these children, failing to detect the behaviors or recognize the behavior features in an accurate manner. To solve the problem, this paper puts forward a novel method to recognize the behavior features of school-age children through video image processing. Firstly, the authors designed a method to extract static behavior features of school-age children from surveillance video images. Next, the behavior features of school-age children were extracted by optical flow method. On this basis, a dual-network flow neural network (DNFNN) was designed, in which the time flow network processes the dense optical flow of multiple continuous frames of the surveillance video, while the spatial flow network treats the region of interest (ROI) in the static frame from the video. After that, the workflow of the DNFNN was introduced in details. Experimental results fully demonstrate the effectiveness of the proposed network. The research findings provide a reference for the application of video image processing to behavior recognition in other fields.

A forensic image processing environment for investigation of surveillance video

2007 ◽  
Vol 167 (2-3) ◽  
pp. 207-212 ◽  
Author(s):  
M. Jerian ◽  
S. Paolino ◽  
F. Cervelli ◽  
S. Carrato ◽  
A. Mattei ◽  
...  
Keyword(s):  
Image Processing ◽  
Surveillance Video

scholarly journals Helmet Detection on Two-Wheelers and Number Plate Recognition using Image Processing

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1822-1824
Keyword(s):  
Image Processing ◽  
Face Detection ◽  
False Alarms ◽  
Sudden Increase ◽  
Surveillance Video ◽  
Safety Equipment ◽  
Transform Method ◽  
Segmentation Methods ◽  
Human Face Detection ◽  
Motorcycle Accidents

There has been a sudden increase in motorcycle accidents over the years. The helmet is a safety equipment that protects the motorcyclists, however many riders (i.e. students) don’t use it and the results could be fatal. This paper holds an agenda to propose a system for detection of motorcyclists without helmet. The proposed idea first detects motorcycle riders using surveillance video using background subtraction and object segmentation methods. We have also used techniques that involve human face detection using image processing. This includes face detection using Haar like feature technique and circular Hough transform method that helps in detecting any circular object hence detecting the helmet. This approach works in the near real time mode with significantly low false alarms.

scholarly journals Smart Surveillance Security Systems 4s for Detection using SIFT and SURF in Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 2418-2420
Keyword(s):  
Image Processing ◽  
Feature Matching ◽  
Daily Life ◽  
Vital Role ◽  
Quantitative Comparison ◽  
Surveillance Video ◽  
Security Systems ◽  
Wide Range ◽  
Smart Surveillance ◽  
Selection Of

Surveillance video is used for security purpose in our daily life in various places. It is used to observe the unusual activity that is taking place around us. Today in most of the shop owners have CCTV cameras to record, the uncertain activities and even it is used in houses in remote places. A system must be smart enough to detect. This paper uses SIFT and SURF algorithm for detection. Image registration is a development in which more than two images from various imaging equipment are reserved at various angles and at various times from the identical prospect and geometrically aligned for further exploration. Data may be from different sensors, CCTV taken at different times, depths, or perspective. Feature-DetectorDescriptor plays a vital role in feature matching application for selection of feature; this paper presents a comparative analysis of SIFT, SURF, algorithms. Experiments have been conducted on a wide range of images taken from datasets. A quantitative comparison is presented. This paper gives an useful ideas for making important decisions and it also helps in providing a smart security system.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

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