DETECTION AND ANALYSIS OF MOVING OBJECTS FOR VIDEO SURVEILLANCE

2005 ◽  
Vol 02 (03) ◽  
pp. 227-239 ◽  
Author(s):  
YOUFU WU ◽  
MO DAI
Keyword(s):  
Video Surveillance ◽  
Moving Objects ◽  
Video Stream ◽  
Moving Object ◽  
Background Model ◽  
Mobile Objects ◽  
Analysis Methods ◽  
Comparison Results ◽  
Slow Moving

In this paper, we address the problem of detection and analysis of moving objects in a video stream obtained by a fixed camera. To detect the moving objects, the tradition method is to create a fixed image first, which includes all the motionless parts of the scene, known as the background model. The difficulty of this approach lies mainly in two aspects: The first relates to the fact that a slow moving object can leave a visible trace in background model. The latter comes from the variation of illumination in the course of time so it cannot obtain a reasonable background model. To overcome these difficulties, we propose a multiple background model. At the exit of the detection of moving objects, the tracking (matching) of a moving object extracted in the successive images is necessary to analyze its behavior. After the matching of mobile objects, a series of analysis methods are presented. The proposed tracking and analysis methods allow dealing with partial occlusions, stopping and going motion, moving directions, crossing of moving object in very challenging situations. The experiment and comparison results are reported for different real sequences, which show better performance of our methods.

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

Block Background Subtraction Method Based on ViBe

2014 ◽  
Vol 556-562 ◽  
pp. 3549-3552
Author(s):  
Lian Fen Huang ◽  
Qing Yue Chen ◽  
Jin Feng Lin ◽  
He Zhi Lin
Keyword(s):  
Spatial Correlation ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object ◽  
Background Model ◽  
Subtraction Method ◽  
Current Frame ◽  
The Difference ◽  
Set Up

The key of background subtraction which is widely used in moving object detecting is to set up and update the background model. This paper presents a block background subtraction method based on ViBe, using the spatial correlation and time continuity of the video sequence. Set up the video sequence background model firstly. Then, update the background model through block processing. Finally employ the difference between the current frame and background model to extract moving objects.

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 Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Li Yao ◽  
Miaogen Ling
Keyword(s):  
Moving Objects ◽  
Color Space ◽  
Video Stream ◽  
Background Model ◽  
Foreground Extraction ◽  
Mixture Of Gaussians ◽  
Frame Difference ◽  
Rgb Color Space ◽  
Computer Vision Applications ◽  
Blob Tracking

Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very crowded situations. What is more, a new shadow removal method based on RGB color space is proposed.

The Research of Motion Detection and Shadow Elimination in Video Surveillance

2012 ◽  
Vol 468-471 ◽  
pp. 2691-2694
Author(s):  
Zhi Li Qing ◽  
Yue Lin Chen
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Moving Objects ◽  
Color Space ◽  
Video Image ◽  
Experimental Results ◽  
Background Model ◽  
Hsv Color Space ◽  
Shadow Elimination

This paper studies the moving objects detect and shadow eliminate in video surveillance. Completed the background generated on the video image by study the mixed Gaussian background model, by transforming the image to hsv color space for processing, which achieve the elimination of shadows. The experimental results show the approach this paper use is effectively on the background generated and shadow remove.

scholarly journals Embedded Video Surveillance Based Moving Object Detection and Tracking

2019 ◽  
Vol 8 (3) ◽  
pp. 5740-5745
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Video Surveillance ◽  
Intelligent Systems ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Superior Performance ◽  
Detection And Tracking ◽  
Gradient Based

Background reckoning and the foreground, play prominent roles in the tasks of visual detection and tracking of objects. Moving Object Detection has been widely used in sundry discipline such as intelligent systems, security systems, video monitoring systems, banking places, provisionary systems, and so on. In this paper proposes moving objects detection and tracking method based on Embedded Video Surveillance. The method is based on using lines computed by a gradient-based optical flow and an edge detector gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for tracking objects using this feature. The proposed method is compared with a recent work, proving its superior performance and when we want to represent high quality videos and images with, lower bit rate, and also suitable for real-world live video applications. This method reduces influences of foreground objects to the background model. The simulation results show that the background image can be obtained precisely and the moving objects recognition is achieved effectively

Method of exact position fix of mobile objects by means of RFID systems and its application

2019 ◽  
pp. 51-58
Author(s):  
B. G. Mayorov
Keyword(s):  
Radio Frequency Identification ◽  
Moving Objects ◽  
Moving Object ◽  
Rfid Tags ◽  
Mobile Objects ◽  
Reader Antenna ◽  
Frequency Identification ◽  
Exact Position ◽  
Identification Tags ◽  
Passive Rfid

The features of the author’s patented method for determining the coordinates of moving objects using radio frequency identification tags (RFID tags) are studied, in which the coordinates of their constant position on the object’s path are recorded. On a moving object (personnel, warehouse forklift, car, etc.), RFID tag readers are installed that interact with RFID tags installed on the ground and read their coordinates. Thereby, the coordinates of a moving object on the track are accurately and quickly determined. A methodology for choosing an implementation option is proposed and examples of applying the obtained results in mines, warehouses, on automobile routes, for civil and dual-use systems are given. The necessity of using passive RFID tags and a circularly polarized reader antenna is established. The resulting solution has no real restrictions on the speed of moving objects.

Moving Objects Detection and Speed Estimation in Intelligent Surveillance System

2013 ◽  
Vol 712-715 ◽  
pp. 2354-2358
Author(s):  
Zhi Hong Xi ◽  
Guang Hui Dong
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Operating Conditions ◽  
Background Model ◽  
Moving Target ◽  
Video Surveillance System ◽  
Target Area ◽  
Intelligent Video Surveillance ◽  
Intelligent Video Surveillance System

In order to solve the problem of highway intelligent video surveillance system for effective monitoring of vehicle operating conditions, a fast block background modeling method is proposed in the framework for intelligent video surveillance system. First using statistical histogram to build the background model of the video surveillance system, second using background subtraction method to locate the moving target area, at last using displacement of the minimum exterior rectangle centroid of the moving target between two frames to calculate moving target speed, without the aid calibration. Experimental results show that the proposed method exhibits its superiority in processing time, the time of building background model through 100 frames is 3.8s. The proposed method has good practical value used in intelligent video surveillance.

scholarly journals U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  
Keyword(s):  
Moving Objects ◽  
Object Segmentation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Moving Object ◽  
Feature Maps ◽  
Moving Object Segmentation ◽  
Practical Applications ◽  
Multi Scale ◽  
Spatial Redundancy

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.

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