ViBe Algorithm Research Articles

Research on visual background extractor to identify the vehicles based on edge similarity

By combining visual background extractor with Canny operator, a new approach to identify vehicles in a complex traffic environment is proposed. Firstly, the foreground object is extracted by ViBe algorithm and background difference algorithm, then the 'ghost' is removed by means of edge similarity. Next, the complete moving objects can be obtained by using morphological processing for the foreground object, which can be used to detect vehicles by combining with motion analysis. Experiments showed that the whole region of the vehicle object in a complex traffic environment can be extracted exactly and effectively by using this method. In addition, problems due ghosts and the variation of background can be well handled with low computation complexity, which can fulfil the needs of real-time operation.

Research on visual background extractor to identify the vehicles based on edge similarity

By combining visual background extractor with Canny operator, a new approach to identify vehicles in a complex traffic environment is proposed. Firstly, the foreground object is extracted by ViBe algorithm and background difference algorithm, then the 'ghost' is removed by means of edge similarity. Next, the complete moving objects can be obtained by using morphological processing for the foreground object, which can be used to detect vehicles by combining with motion analysis. Experiments showed that the whole region of the vehicle object in a complex traffic environment can be extracted exactly and effectively by using this method. In addition, problems due ghosts and the variation of background can be well handled with low computation complexity, which can fulfil the needs of real-time operation.

基于ViBe的改进运动目标检测算法

ViBe是运动目标检测的常用算法。针对ViBe运动检测算法在实际环境中易出现鬼影、动态背景等干扰的问题，提出结合灰度特征直方图匹配的方法来检测鬼影，并进一步消除鬼影的干扰。针对动态背景错检测为运动前景的问题，为图像中各个像素设置闪烁属性和突变属性来判断各像素是否属于动态背景。实验表明，改进后的算法能有效弥补传统ViBe算法在鬼影消除和动态背景干扰上的不足。 Background subtraction is a common algorithm for moving target detection. Aiming at the prob-lem that the motion detection algorithm of ViBe is prone to ghost and dynamic background in the real environment, it is proposed to detect the ghost by combining the gray feature histogram matching method and further eliminate the ghost interference. Aiming at the problem that motion background is detected as motion foreground, we set the flickering property and mutation property for each pixel in the image to judge whether each pixel belongs to dynamic background. Experiments show that the improved algorithm can effectively compensate for the deficiencies of traditional ViBe algorithm in ghosting and dynamic background jamming.

改进的ViBe算法及其在交通视频处理中的应用

改进的ViBe算法及其在交通视频处理中的应用

Multi-pedestrian Detection Approach Based on Improved ViBe Algorithm

Multi-pedestrian Detection Approach Based on Improved ViBe Algorithm

A novel object detection technique for dynamic scene and static object

The object detection in video streams plays an important role in computer vision applications. The background subtraction, comparing each new frame to a model of the background, is one of the most popular method. However, the static background is practically impossible, and dynamic background makes the perfect object detection difficult. In addition, the problem of static object significantly affects the performance. In the paper, based on the visual background extraction (ViBe) algorithm, we presented a new method to deal with all problems. For the dynamic scene, we presented a new update mechanism to obtain the more robust model in dynamic regions. To address the static object issue, we cancelled the propagation mechanism in ViBe, designed an algorithm to detect region where it is always detected as foreground, and distinguished the static object from ghost with a self-designed measurement, which combined the knowledge of region contrast and edge. We described our method in full details, and compared it with other background subtraction techniques. Our experimental results show that the proposed method outperforms several state-of-the-art object detection approaches. In addition, it can process 60 frames per second on an Intel i5 3.1 GHz CPU with C++ code.

A New IC Solder Joint Inspection Method for an Automatic Optical Inspection System Based on an Improved Visual Background Extraction Algorithm

In the field of automatic optical inspection (AOI), defect recognition for an integrated circuit (IC) solder joint is a long-standing task. Inspired by a visual background extraction (ViBe) algorithm, an object detection method in computer vision, we propose a new inspection method for IC solder joints with an improved ViBe algorithm. To the best of our knowledge, we are the first to consider the defect inspection problem as an object detection problem. We build a solder joint model using the ViBe model updating scheme. Then, we compare the solder joint image with the well-trained model to detect potential defects. Finally, we introduce a frequency map method and define a metric named defect degree to evaluate the qualities of the solder joints. Experimental results show that our method is universal, accurate, and easily debugged compared with the other existing methods.

Improved Adaptive Vibe and the Application for Segmentation of Complex Background

To solve the problems that basic Vibe algorithm cannot effectively eliminate the influence of background noise, follower shadow, and ghost under complex background effectively, an adaptive threshold algorithm, AdaVibe, based on the framework of basic Vibe is proposed. Aiming at the shortage of the basic algorithm, this paper puts forward some improvement measures in threshold setting, shadow eliminating, and ghost suppression. Firstly, judgment threshold takes adjustment with the changes of background. Secondly, a fast eliminating ghost algorithm depending on adaptive threshold is introduced. Finally, follower shadow is detected and inhibited effectively through the gray properties and texture characteristics. Experiments show that the proposed AdaVibe algorithm works well in complex environment without affecting computing speed and has stronger robustness and better adaptability than the basic algorithm. Meanwhile, the ghost and follower shadow can be absorbed quickly as well. Therefore, the accuracy of target detection is effectively improved.

Moving Object Detection Based on Improved ViBe Algorithm

In this paper, an improved ViBe background subtraction algorithm is proposed for dealing with ghost problem during the process of moving object detection. The ghost areas in image can be detected based on the theory that the histogram distributions of ghost areas have similarity distribution characteristics. However, the histogram distributions of real moving objects change with the real objects moving. The influence of ghost areas on moving object detection is eliminated. The improved ViBe, the original ViBe and the Gauss mixture model are compared and analyzed, The results show that the improved ViBe can effectively eliminate ghost areas, and has high real-time.

Real-time implementation of foreground object detection from a moving camera using the ViBe algorithm

The article presents a real-time hardware implementation of a foreground object detection for a non-static camera setup. The system consists of two parts: the calculation of the displacement between two consecutive frames using a correlation based corner tracker and background generation method ViBE (Visual Background Extractor). The paper discusses details of the used hardware modules, resource utilization, computing performance and power dissipation. The solution was evaluated on sequences recorded with a static and moving camera. The system was successfully tested on a hardware platform with an FPGA device. It allows to process a 720x576 pixels and 50 frames per second video stream in real-time.

STRAY DOG DETECTION IN WIRED CAMERA NETWORK

Existing surveillance systems impose high level of security on humans but lacks attention on animals. Stray dogs could be used as an alternative to humans to carry explosive material. It is therefore imperative to ensure the detection of stray dogs for necessary corrective action. In this paper, a novel composite approach to detect the presence of stray dogs is proposed. The captured frame from the surveillance camera is initially pre-processed using Gaussian filter to remove noise. The foreground object of interest is extracted utilizing ViBe algorithm. Histogram of Oriented Gradients (HOG) algorithm is used as the shape descriptor which derives the shape and size information of the extracted foreground object. Finally, stray dogs are classified from humans using a polynomial Support Vector Machine (SVM) of order 3. The proposed composite approach is simulated in MATLAB and OpenCV. Further it is validated with real time video feeds taken from an existing surveillance system. From the results obtained, it is found that a classification accuracy of about 96% is achieved. This encourages the utilization of the proposed composite algorithm in real time surveillance systems.