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.

Vehicle Detection Method under Night Circumstance

2013 ◽  
Vol 380-384 ◽  
pp. 3870-3873 ◽  
Author(s):  
Lin Guo ◽  
Xiang Hui Shen
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Detection Method ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Intelligent Vehicle ◽  
Urban Street ◽  
Light Interference ◽  
Frame Difference

In intelligent vehicle detection, vehicle detection at night especially detection in the condition of urban street always remains a problem. This paper proposes an effective vehicle detection algorithm. Firstly it extracts effective vehicle edge by the method of embossment which eliminates light interference. Then we detect the vehicle moving area by frame difference method and calculate the threshold by OTSU algorithm. Finally the noise points are removed by erosion and expansion. This method can better extract the moving objects.

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 of Moving Objects Detection Method for Examination Room Surveillance Based on Background Subtraction

2013 ◽  
Vol 718-720 ◽  
pp. 385-388
Author(s):  
Yong Zheng Lin ◽  
Pei Hua Liu
Keyword(s):  
Background Subtraction ◽  
Surveillance System ◽  
Moving Objects ◽  
Detection Method ◽  
Detection Algorithm ◽  
System Stability ◽  
Moving Objects Detection ◽  
System A ◽  
Objects Detection ◽  
Examination Room

Detection of moving objects is one of the primary factors to influence the examination surveillance system. A new moving objects detection algorithm based on background subtraction is presented after the introduction various of existing methods. Dynamic threshold conception is put forward while defining threshold. Practices show that this method can successfully overcome lighting variations and the system stability is improved.

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.

scholarly journals Extended Codebook with Multispectral Sequences for Background Subtraction

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 703 ◽  
Author(s):  
Rongrong Liu ◽  
Yassine Ruichek ◽  
Mohammed El-Bagdouri
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Statistical Information ◽  
Color Model ◽  
Adaptive Mechanism ◽  
Spectral Distortion ◽  
Spectral Distance ◽  
Information Divergence ◽  
Adaptive Codebook ◽  
Self Adaptive

: The Codebook model is one of the popular real-time models for background subtraction. In this paper, we first extend it from traditional Red-Green-Blue (RGB) color model to multispectral sequences. A self-adaptive mechanism is then designed based on the statistical information extracted from the data themselves, with which the performance has been improved, in addition to saving time and effort to search for the appropriate parameters. Furthermore, the Spectral Information Divergence is introduced to evaluate the spectral distance between the current and reference vectors, together with the Brightness and Spectral Distortion. Experiments on five multispectral sequences with different challenges have shown that the multispectral self-adaptive Codebook model is more capable of detecting moving objects than the corresponding RGB sequences. The proposed research framework opens a door for future works for applying multispectral sequences in moving object detection.

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.

Vehicle Statistics and Retrograde Detection Based on Characteristic Analysis

2014 ◽  
Vol 556-562 ◽  
pp. 2672-2676
Author(s):  
Qing Ye ◽  
Li Zhang ◽  
Yong Mei Zhang
Keyword(s):  
Moving Objects ◽  
Detection Method ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Object Motion ◽  
Characteristic Analysis ◽  
Moving Vehicles ◽  
Vehicle Motion ◽  
Vehicle Trajectory ◽  
Motion Characteristics

Vehicle detection in the traffic monitoring system has been widely studied in recent years. This paper presents a vehicle detection method based on different object motion characteristics to track the moving vehicles. Firstly, we use the frame different method to detect the moving objects. Secondly, we do binarization and filtering processing with the adaptive threshold segmentation technique to extract the moving vehicles in the traffic video. Then we determine centroid displacement to recognize the vehicle motion characteristics for retrograde judgment. Finally, we draw the vehicle trajectory and make the vehicle motion statistics. Experiment results show that the method can real-time track the moving vehicles and accurately get the vehicle retrograde detection.

scholarly journals COMPARISON OF BACKGROUND SUBTRACTION, SOBEL, ADAPTIVE MOTION DETECTION, FRAME DIFFERENCES, AND ACCUMULATIVE DIFFERENCES IMAGES ON MOTION DETECTION

SINERGI ◽  
2018 ◽  
Vol 22 (1) ◽  
pp. 51
Author(s):  
Dara Incam Ramadhan ◽  
Indah Permata Sari ◽  
Linna Oktaviana Sari
Keyword(s):  
Image Processing ◽  
Object Recognition ◽  
Motion Detection ◽  
Background Subtraction ◽  
Detection Method ◽  
Detection Methods ◽  
Lighting Conditions ◽  
Frame Difference ◽  
Best Value ◽  
Adaptive Motion

Nowadays, digital image processing is not only used to recognize motionless objects, but also used to recognize motions objects on video. One use of moving object recognition on video is to detect motion, which implementation can be used on security cameras. Various methods used to detect motion have been developed so that in this research compared some motion detection methods, namely Background Substraction, Adaptive Motion Detection, Sobel, Frame Differences and Accumulative Differences Images (ADI). Each method has a different level of accuracy. In the background substraction method, the result obtained 86.1% accuracy in the room and 88.3% outdoors. In the sobel method the result of motion detection depends on the lighting conditions of the room being supervised. When the room is in bright condition, the accuracy of the system decreases and when the room is dark, the accuracy of the system increases with an accuracy of 80%. In the adaptive motion detection method, motion can be detected with a condition in camera visibility there is no object that is easy to move. In the frame difference method, testing on RBG image using average computation with threshold of 35 gives the best value. In the ADI method, the result of accuracy in motion detection reached 95.12%.

scholarly journals Automatic Vehicles Detection, Classification and Counting Techniques / Survey

2020 ◽  
pp. 1811-1822
Author(s):  
Mustafa Najm ◽  
Yossra Hussein Ali
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Background Subtraction ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Condition ◽  
Real Time Processing

Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Traffic monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed,  with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

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