Improved Moving Target Detection Technology

2014 ◽  
Vol 971-973 ◽  
pp. 1628-1632 ◽  
Author(s):  
Xiao Hui Jin ◽  
Wei Yang ◽  
Qian Jin Liu ◽  
Di Zhao ◽  
Sheng Xu
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Detection System ◽  
Ccd Camera ◽  
Target Image ◽  
Target Region ◽  
Difference Image ◽  
Frame Difference ◽  
Detection Technology ◽  
The Difference

In order to detect target clearly, a detection system based on DM642 was designed. The system used improved frame-difference method combined with the background subtraction to detect target. First, the CCD camera scanned the surroundings step by step, then the background model was built, and improved three-frame-difference method was used to get the three-frame-difference image. The target image was the difference of target region extracted by three-frame-difference method and the target region extracted by background subtraction method. Experiments showed that the target image had less interference and a clear profile.

A Motion Image Detection Method Based on the Inter-Frame Difference Method

2014 ◽  
Vol 490-491 ◽  
pp. 1283-1286 ◽  
Author(s):  
Yuan Hang Cheng ◽  
Jing Wang
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Background Subtraction ◽  
Vision System ◽  
Robot Vision ◽  
Main Idea ◽  
Image Method ◽  
Detection Model ◽  
Difference Image ◽  
Frame Difference

Mobile robot vision system based on image information on environment, to make it automatic separation from obstacles and achieve precise mathematical description of obstacles, we construct detection model which combined by the frame difference method and background subtraction for target detection, comprehensive utilization of the main idea of three frame difference image method, the background subtraction and frame difference method combined to complement each other, thereby overcoming each other's weaknesses and improving the effect of target detection, experiment results show that this method can effectively improve the efficiency of target detection.

The Design of Real-Time Face Detection System Based on FPGA

2013 ◽  
Vol 473 ◽  
pp. 231-234
Author(s):  
Su Hua Chen ◽  
Xu Fang ◽  
Yong Guang Liu ◽  
Jun Wang
Keyword(s):  
Processing Speed ◽  
Detection System ◽  
Main Body ◽  
Infrared Source ◽  
Difference Image ◽  
Small Influence ◽  
The Face ◽  
The Difference ◽  
Locating System ◽  
Image Difference

The design attempts for thefirst time to realize face locating system on the FPGA platform using themethod combined initiative infrared source with image difference. Through imagedifference process, the system obtains a difference image without backgroundinterference which takes the face as the main body. It can obtain the personface boundary by projecting the difference image in the horizontal and verticaldirection. The system processing speed amount s to the video source frequency25 frame per second, satisfying the timely request; the method of initiativeinfrared source makes the exterior have small influence on the image andguarantees the robustness of the system.

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.

Object Tracking Algorithm Based on Camshift Combining Background Subtraction with Three Frame Difference

2013 ◽  
Vol 373-375 ◽  
pp. 1116-1119 ◽  
Author(s):  
Quan Tang ◽  
Shu Guang Dai ◽  
Jie Yang
Keyword(s):  
Probability Distribution ◽  
Object Tracking ◽  
Difference Method ◽  
Background Subtraction ◽  
Tracking Algorithm ◽  
Target Area ◽  
Characteristic Parameters ◽  
Subtraction Method ◽  
Frame Difference ◽  
Camshift Algorithm

Camshift tracking algorithm is based on probability distribution of color , it is susceptible to be interfered by the same color in the background, which will lead to the failure of the target tracking. To overcome this problem it presented an improved Camshift tracking algorithm. It combined background subtraction method with three frame difference method to detect target, got rectangular characteristic parameters of the motion target area as the Camshift initialization parameters, replaced the general Camshift algorithm which is based on color feature. Experimental results show that Camshift algorithm combining the background subtraction method with three frame difference method can meet the requirements of the real-time and stability to a certain extent.

A Method for Traffic Collision Detection

2012 ◽  
Vol 220-223 ◽  
pp. 2606-2610
Author(s):  
Dong Yin ◽  
Fan Zhang ◽  
Kun Wang
Keyword(s):  
Prior Knowledge ◽  
Collision Detection ◽  
Difference Method ◽  
Traffic Accident ◽  
Detection Method ◽  
Vehicle Tracking ◽  
Video Images ◽  
Frame Difference ◽  
Vehicle Collision ◽  
Detection Technology

This paper presents a detection method for traffic accident in real-time video images. Firstly, according to improved average background model, frame difference method and edge detection technology are used to detect vehicles. Secondly, vehicle tracking is accomplished by matching the distance, area and histogram of the same vehicle in next frame. Finally, using the concept of collision area and key point as pre-qualification, the situation of vehicle collision will be accurately detected by prior knowledge and histogram information. The experiments results show that our method is effective.

Moving Object Detection and Tracking Algorithm Based on Background Subtraction

2012 ◽  
Vol 263-266 ◽  
pp. 2211-2216
Author(s):  
Qing Ye ◽  
Yong Mei Zhang
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Object Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Difference Image ◽  
Detection And Tracking ◽  
The Difference ◽  
Background Image ◽  
Static Background

Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.

Fusion of Background Subtraction and Frame Difference in HSV Space for Multi-Object Detection

2013 ◽  
Vol 380-384 ◽  
pp. 3895-3899
Author(s):  
Ya Ne Wen ◽  
Hong Song Li ◽  
Hao Zhou ◽  
Li Ping Tang ◽  
Jun Qi She
Keyword(s):  
Difference Method ◽  
Background Subtraction ◽  
Gaussian Mixture ◽  
Test Results ◽  
Subtraction Method ◽  
False Detection ◽  
Strong Light ◽  
Fusion Frame ◽  
Frame Difference ◽  
Light And Shadow

in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.

A Motion Vehicle Detection Method Based on Self-Adaptive Background Subtraction with Cumulative Inter-Frame Difference

2013 ◽  
Vol 655-657 ◽  
pp. 890-894 ◽  
Author(s):  
Hong Zheng ◽  
Wen Ju An ◽  
Zhen Li
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Detection Method ◽  
Vehicle Detection ◽  
Frame Difference ◽  
Detection Technology ◽  
Binary Object ◽  
Object Mask ◽  
Inter Frame ◽  
Self Adaptive

Against the poor accuracy of the vehicle counters extracted by existing vehicle detection technology, a motion vehicle detection method based on self-adaptive background subtraction with cumulative inter-frame difference is proposed in this paper. Cumulative inter-frame difference is used to subtract binary object mask. According to the binary object mask, in the area of moving objects the pixels of last background are used to modify the current background, otherwise the pixels of current image are used. The result of this operation is the current background. Then the background difference method is used to detect moving vehicles.

Detection of Indoor Robot’s Passable Areas Based on a Single-Line Structured Light

2013 ◽  
Vol 433-435 ◽  
pp. 667-675
Author(s):  
Xiao Rong Mao ◽  
Xia Yuan ◽  
Chun Xia Zhao
Keyword(s):  
Path Planning ◽  
Full Advantage ◽  
High Precision ◽  
Difference Method ◽  
Detection System ◽  
Structured Light ◽  
Single Line ◽  
Original Image ◽  
The Difference ◽  
Interesting Area

Center extraction of structured light’s stripe is a key link in the detection system for the robot’s passable areas based on a single-line structured light. With the original image that the camera took, we used the difference method to quickly get the interesting area where the light stripe was. Then we combined the Hough transformation with the improved K-means algorithm to take full advantage of the effective information of light stripe. We extracted the structured light’s center with high precision. It provides a guarantee for the robots’ passable area’s detection and path planning.

Sign in / Sign up

Export Citation Format

Share Document