An effective motion object detection using adaptive background modeling mechanism in video surveillance system

2021 ◽  
pp. 1-13
Author(s):  
SivaNagiReddy Kalli ◽  
T. Suresh ◽  
A. Prasanth ◽  
T. Muthumanickam ◽  
K. Mohanram
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Detection System ◽  
Research Work ◽  
Traffic Monitoring ◽  
Background Modeling ◽  
Video Surveillance System ◽  
Fuzzy C Means

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

scholarly journals Recognition and Tracking of Moving Objects Under Video Surveillance Using Matlab

2020 ◽  
pp. 199-205
Author(s):  
K. Sirisha ◽  
B. Praneetha ◽  
K. Preethi ◽  
D. Saidha ◽  
M. Jyothirmai
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Complete Information ◽  
Traffic Monitoring ◽  
Video Surveillance System ◽  
Motion Information ◽  
Automotive Safety ◽  
Computer Vision Applications ◽  
Over Time

<p>Now-a-days its becoming very important to know about any information in a digital way in order to avoid crimes and mischief activites. Conventional methods that are used now do have limited advantages we have to check for every minute information in thorough way. By using this method, we need not put effort of many hours over monitors, instead it gives the complete information about what, how and when that happened over there. This method shows how to perform automatic detection and motion-based tracking of moving objects in a video from stationary camera. Detection of motion-based tracking are important components of many computer vision applications including activity recognition, traffic monitoring and automotive safety The problem of motion based object tracking can be divided into two parts:</p> <p>[1]Detecting moving objects in each frame.</p> <p>[2]Associating the detections corresponding to the same object over time.</p> <p>For instance in a video surveillance system that aims to identify people based on their motion information, it is essential to accurately detect the moving objects and then use a robust algorithm to track them. In terms of accuracy and efficiency, the proposed method is shown to be highly accurate in determning the number of moving objects and also fast in tracking them in the scene.</p>

scholarly journals A deep learning approach to building an intelligent video surveillance system

2020 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Face Recognition ◽  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Single Shot ◽  
Convolutional Networks ◽  
Starting Point ◽  
Intelligent Video Surveillance System

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.

The Design and Implementation of Moving Object Detecting Algorithm in Intelligent Video Surveillance

2015 ◽  
Vol 738-739 ◽  
pp. 779-783
Author(s):  
Jin Hua Sun ◽  
Cui Hua Tian
Keyword(s):  
Video Surveillance ◽  
Background Subtraction ◽  
Surveillance System ◽  
Image Sequence ◽  
Background Modeling ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Video ◽  
Subtraction Method ◽  
Design And Implementation

In view of the problems existed in moving object detection in video surveillance system, background subtraction method is adopted and combined with Surendra algorithm for background modeling, an algorithm of detecting moving object from video is proposed, and OpenCV programming is adopted in Visual c ++ 6.0 for implementation. Experimental results indicate that the algorithm can accurately detect and identify moving object in video by reading the image sequence of surveillance video, the validity of the algorithm is verified.

The Application of the Moving Object Detection in HD Video Surveillance System

2013 ◽  
Vol 385-386 ◽  
pp. 1509-1512
Author(s):  
Lian Li ◽  
Yong Peng Liu
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Processing System ◽  
Moving Object ◽  
Video Surveillance System ◽  
High Definition ◽  
Motion Direction ◽  
Distribution Features ◽  
Sign Distribution

Today the existing image processing systems widely used standard definition resolution. Which is not enough distinct. High definition (HD) and intelligence gradually become the developing trend of the image acquisition and processing system. Motion detection plays an important role in video surveillance system. The sign distribution features will be covered up by the use of the absolute differential image. In this article, a method to determine the motion direction of moving objects by using the sign distribution features in the differential image of two consecutive frames is proposed. To extract the characteristics of the moving object regions,Other parts as the background image is still. The transmission should been stopped, if there is no moving object. These should save storage space and reduce the demand for network speed. Experimental results show that algorithm of the method is suitable for computer processing.

scholarly journals An Indoor Video Surveillance System with Intelligent Fall Detection Capability

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Chih Chen ◽  
Yang-Ming Liu
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Fall Detection ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Video Surveillance System ◽  
K Nearest Neighbor ◽  
Human Movements

This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN) algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.

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.

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