Detecting moving objects by background difference and frame-difference

2007 ◽  
Author(s):  
Yihuan Zhao ◽  
Zulin Wang
Keyword(s):  
Moving Objects ◽  
Frame Difference ◽  
Background Difference

Adaptive Moving Vehicle Detection Algorithm Based on Hybrid Background Subtraction and Frame Difference

2014 ◽  
Vol 644-650 ◽  
pp. 930-933 ◽  
Author(s):  
Yan Li Luo ◽  
Han Lin Wan ◽  
Li Xia Xue ◽  
Qing Bin Gao
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Target Image ◽  
Moving Vehicle ◽  
Frame Difference ◽  
Video Frequency ◽  
Background Difference ◽  
Moving Vehicle Detection ◽  
Initial Target

This paper proposes an adaptive moving vehicle detection algorithm based on hybrid background subtraction and frame difference. The background image of continuous video frequency is reconstructed by calculating the maximun probability grayscale using grey histogram; Moving regions is gained by frame defference, the initial target image is obtained by background difference method,moving regions image and initial target image AND,XOR and OR operations to get the vehicle moving target images. Experimental results show that the algorithm can response timely to the actual scene changes and improve the quality of moving vehicle detection.

Study on Moving Object Detection Based on RGB Color Model

2013 ◽  
Vol 710 ◽  
pp. 700-703
Author(s):  
Chun Yang Liu ◽  
Dao Zheng Hou ◽  
Chang An Liu
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Color Image ◽  
Three Dimensional ◽  
Color Model ◽  
Current Frame ◽  
Gray Image ◽  
Average Value ◽  
The Difference ◽  
Background Difference

The traditional background difference method is based on gray image. Some information is lost when color image is transformed into gray image. So it is difficult to discriminate different colors with similar gray values and easily disturbed by noise and shadows. In this paper, the background difference is based on RGB color model. It is proposed to use the average value of each pixel of the color image sequences to extract the background, and then use the three-dimensional color values of the current frame and background image to compute the difference to detect the moving objects. The proposed approach is simple and easy to implement. The experimental results show that it is more sensitive to colors and has higher accuracy and robustness than the traditional background difference method. Besides, it is more resistant to shadows.

scholarly journals Detection of Vessel on UAV based on Segmentation Using Edge Based Dilation

2018 ◽  
Vol 7 (2) ◽  
pp. 129-136
Author(s):  
Muhammad Khaerul Naim Mursalim
Keyword(s):  
Moving Objects ◽  
Moving Object ◽  
Image Descriptor ◽  
Uncertainty Factor ◽  
The Past ◽  
Complex Processes ◽  
Sobel Operator ◽  
Frame Difference ◽  
Environment Type ◽  
Edge Based

UAV usually is used in military field for reconnaissance, surveillance, and assault. To detect a moving object in real-time like vessel, there are complex processes than to detect the object that does not moving. There are some issues that faced in detection process of moving object in UAV, called constraint uncertainty factor (UCF) such as environment, type of object, illumination, camera of UAV, and motion. One of the practical problems that become concern of researchers in the past few years is motion analysis. Motion of an object in each frame carries a lot of information about the pixels of moving objects which has an important role as the image descriptor. In this paper, we use SUED (Segmentation using edge-based dilation) algorithm to detect vessel. The concept of the SUED algorithm is combining the frame difference and segmentation process to obtain optimal results. This research showed that the SUED method having problem to detect the vessel even though we combine it with sobel operator. using the combination of wavelet and Sobel operator on the detection of edges obtained increasing in the number of DR about 3%, but then FAR also increased from 41.23% to 52.09%.

A Moving Objects Detection Algorithm Based on Three-Frame Difference and Sparse Optical Flow

2014 ◽  
Vol 13 (11) ◽  
pp. 1863-1867 ◽  
Author(s):  
Guo-Wu Yuan ◽  
Jian Gong ◽  
Mei-Ni Deng ◽  
Hao Zhou ◽  
Dan Xu
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Frame Difference ◽  
Objects Detection

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.

scholarly journals Method of Multiobject Detecting and Tracking Based on DM643

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yitao Liang ◽  
Deshan Zhang ◽  
Feng Wang ◽  
Yonggang Li ◽  
Meng Zhang
Keyword(s):  
Mathematical Morphology ◽  
Video Sequence ◽  
Moving Objects ◽  
Research Subject ◽  
Important Research ◽  
Running Speed ◽  
Hardware Platform ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Background Difference

The technology of moving objects detection has become an important research subject for its extensive application prospect. In this paper, it is presented that interframe difference algorithm and background difference algorithm are combined to update the background. The algorithm can deal with the flaw of background difference algorithm. The mathematical morphology method is employed to denoise the image, which may be helpful to improve the accuracy of the detection. The Pyramid algorithm is used to compress each frame data of video sequence. Then, the detecting and tracking of moving objects are tested on the hardware platform (DM643) and the software frame (RF5). The running speed is about 3 times faster than before. The result shows that the accuracy demanded by the detection is met. This method can provide a useful reference for similar application.

An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association

2013 ◽  
Vol 427-429 ◽  
pp. 1822-1825
Author(s):  
Zhen Hai Wang ◽  
Ki Cheon Hong
Keyword(s):  
Object Tracking ◽  
Energy Minimization ◽  
Moving Objects ◽  
Data Association ◽  
Multiple Object Tracking ◽  
Object Extraction ◽  
Frame Difference ◽  
Multiple Object ◽  
Important Research Topic ◽  
Key Steps

multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.

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