scholarly journals APPLYING EDGE DENSITY BASED REGION GROWING WITH FRAME DIFFERENCE FOR DETECTING MOVING OBJECTS IN VIDEO SURVEILLANCE SYSTEMS

2014 ◽  
Vol 03 (04) ◽  
pp. 693-699
Author(s):  
Abhilash K. Sonara .
Keyword(s):  
Video Surveillance ◽  
Moving Objects ◽  
Region Growing ◽  
Edge Density ◽  
Surveillance Systems ◽  
Frame Difference

scholarly journals Object extraction in video sequences based on spatiotemporal independent component analysis

2021 ◽  
Author(s):  
Zhenhe Chen
Keyword(s):  
Independent Component Analysis ◽  
Video Processing ◽  
Moving Objects ◽  
Research Work ◽  
Region Growing ◽  
Component Analysis ◽  
Independent Component ◽  
Video Sequences ◽  
Object Extraction ◽  
Surveillance Systems

Video object extration is one of the most important areas of video processing in which objects from video sequences are extracted and used for many applications such as surveillance systems, pattern recognition etc. In this research work, an object-based technique based on the spatiotemporal independent component analysis (stICA) is developed to extract moving objects from video sequences. Using the stICA, the preliminary source images containing moving objects in the video sequence are extracted. These images are processed using wavelet analysis, edge detection, region growing and multiscale segmentation techniques to improve the accuracy of the extracted objects. A novel compensation method is applied to deal with the nonlinear problem caused by the application of the stICA directly to the video sequences. The recovered objects are indexed by the singular calue decompensation (SVD) and linear combination analysis. Simulation results demonstrate the effectiveness of the stICA-based object extraction technique in content-based video processing applications.

scholarly journals Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review

2018 ◽  
Vol 27 (02) ◽  
pp. 1830001 ◽  
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Objects Tracking ◽  
Object Appearance ◽  
Human Operators ◽  
Active Research

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance.

A New Modeling Approach for the Video Pre-Analysis of Video Surveillance Systems

2021 ◽  
Vol 12 (3) ◽  
pp. 21-34
Author(s):  
Hocine Chebi
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Analysis Method ◽  
Modeling Approach ◽  
Occluded Objects ◽  
Analysis Technique

The work presented in this paper aims to develop a new architecture for video surveillance systems. Among the problems encountered when tracking and classifying objects are groups of occluded objects. Simplifying the representation of objects leads to other reliable object tracking with smaller amounts of information used but protection of the necessary characteristics. Therefore, modeling moving objects into a simpler form can be considered a pre-analysis technique. Objects can be represented in different ways, and the choice of the representation of an object strongly depends on the field of application. An example of a video surveillance system respecting this architecture and using the pre-analysis method is proposed.

VIDEO-BASED APPROACH FOR DETECTING PROHIBITED ACTIVITIES ON SPORTING COURTS

2013 ◽  
Vol 13 (04) ◽  
pp. 1350019
Author(s):  
SHUENN-JYI WANG ◽  
CHUNG-KAI HSIEH ◽  
TSORNG-LIN CHIA
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Moving Objects ◽  
Activity Analysis ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Activity Detection ◽  
Second Stage ◽  
Subtraction Procedure ◽  
Surveillance Cameras

Video surveillance cameras are ubiquitous this decade. With the popularization of sports, costly courts are built widespread. To protect the sport ground against from damage, some activities, such as biking and in-line skating, are prohibited in the court. Traditional video surveillance systems can not prevent these activities in time. In this paper, we propose a video-based approach for detecting prohibited activities that can damage a court. The approach involves prohibited activity analysis and prohibited activity detection. The first stage generates the leg-angle curves of a prohibited activity. A background subtraction procedure is applied to extract moving objects; moving objects are then normalized to minimize influences of various scaling conditions. Leg-angle curves of prohibited activities are generated by computing leg angles. The second stage detects specific prohibited activities by analyzing leg-angle curves obtained from the input video sequences. Our proposed method can detect the prohibited activities in a court, thus preventing such activities in time.

scholarly journals Statistical Model For Pseudo-Moving Objects Recognition In Video Surveillance Systems

2014 ◽  
Vol XL-3 ◽  
pp. 351-356
Author(s):  
B. Vishnyakov ◽  
A. Egorov ◽  
S. Sidyakin ◽  
I. Malin ◽  
Y. Vizilter
Keyword(s):  
Logistic Regression ◽  
Statistical Model ◽  
Decision Rule ◽  
Video Surveillance ◽  
Moving Objects ◽  
Statistical Approach ◽  
Bayes Theorem ◽  
Surveillance Systems ◽  
Video Databases

This paper considers a statistical approach to define pseudo-moving (false) objects in video surveillance systems by constructing systems of hypothesis with the criteria based on statistical behavioral particularities. The obtained results are integrated in two ways: using the Bayes’ theorem or the logistic regression. FAR-FRR curves are plotted for each system of hypothesis and also for the decision rule. The results of the proposed methods are obtained on test video databases.

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