scholarly journals Detection of Moving Objects with Fuzzy Color Coherence Vector

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-Long Qiao ◽  
Kai-Long Yuan ◽  
Chun-Yan Song ◽  
Xue-Zhi Xiang
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Spatial Relationship ◽  
Experimental Results ◽  
Fuzzy Membership ◽  
Subtraction Method ◽  
Color Distribution ◽  
Popular Method ◽  
Statistical Feature ◽  
Advanced Applications

Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence subvectors and color incoherence subvectors, we develop a region-based fuzzy statistical feature for each pixel based on the fuzzy membership matrices. The features are extracted from consecutive frames to build the background model and detect the moving objects. The experimental results demonstrate the effectiveness of the proposed approach.

A Background Subtraction Method for Defect Detection of Printed Image

2013 ◽  
Vol 462-463 ◽  
pp. 421-427
Author(s):  
Jian Hua Ding ◽  
Yao Lu ◽  
Wei Huang ◽  
Ming Qin
Keyword(s):  
Defect Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Texture Feature ◽  
Background Modeling ◽  
Experimental Results ◽  
Subtraction Method ◽  
Moving Objects Detection ◽  
Hsv Histogram ◽  
Objects Detection

Background subtraction is often used to detect the moving objects from static cameras. The difficult of defect detecting of printing matter is how to detect the unknown flaws in complicate background effectively. Inspired by the background modeling approach for moving objects detection, a background modeling method in defect detection of printed image is suggested in this paper. Those pixels without defects are regarded as background, while the flaw pixels are defined as foreground. Firstly, we select LBP histogram as texture feature and HSV histogram as color feature to model the background respectively. Then, lots of printed images in which there are no defects are used to update these two models. Finally, we utilize the models to detect defects of printing images. Experimental results show that this background model works well in our defect detection.

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.

scholarly journals Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance

Algorithms ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 115 ◽  
Author(s):  
Tianming Yu ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Background Subtraction ◽  
Moving Objects ◽  
High Accuracy ◽  
Clustering Method ◽  
Subtraction Method ◽  
Video Scene ◽  
Pumping Unit

Background subtraction plays a fundamental role for anomaly detection in video surveillance, which is able to tell where moving objects are in the video scene. Regrettably, the regular rotating pumping unit is treated as an abnormal object by the background-subtraction method in pumping-unit surveillance. As an excellent classifier, a deep convolutional neural network is able to tell what those objects are. Therefore, we combined background subtraction and a convolutional neural network to perform anomaly detection for pumping-unit surveillance. In the proposed method, background subtraction was applied to first extract moving objects. Then, a clustering method was adopted for extracting different object types that had more movement-foreground objects but fewer typical targets. Finally, nonpumping unit objects were identified as abnormal objects by the trained classification network. The experimental results demonstrate that the proposed method can detect abnormal objects in a pumping-unit scene with high accuracy.

scholarly journals AN ADAPTIVE BACKGROUND UPDATION AND GRADIENT BASED SHADOW REMOVAL METHOD

2013 ◽  
pp. 288-293
Author(s):  
SUMIT KUMAR SINGH ◽  
MAGAN SINGH
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Object Segmentation ◽  
Moving Object ◽  
Shadow Removal ◽  
Subtraction Method ◽  
Moving Object Segmentation ◽  
Shadow Elimination ◽  
Illumination Changes ◽  
Gradient Based

Moving object segmentation has its own niche as an important topic in computer vision. It has avidly being pursued by researchers. Background subtraction method is generally used for segmenting moving objects. This method may also classify shadows as part of detected moving objects. Therefore, shadow detection and removal is an important step employed after moving object segmentation. However, these methods are adversely affected by changing environmental conditions. They are vulnerable to sudden illumination changes, and shadowing effects. Therefore, in this work we propose a faster, efficient and adaptive background subtraction method, which periodically updates the background frame and gives better results, and a shadow elimination method which removes shadows from the segmented objects with good discriminative power. Keywords- Moving object segmentation,

Intelligent Monitoring of Key Sections in Public Transportation

2014 ◽  
Vol 722 ◽  
pp. 353-358
Author(s):  
Yong Wu Wu ◽  
You Fu Wu ◽  
Zu Feng Fu ◽  
Shu Qu Qian
Keyword(s):  
Behavior Analysis ◽  
Hough Transform ◽  
Public Transportation ◽  
Background Subtraction ◽  
Moving Objects ◽  
Experimental Results ◽  
Advanced Stage ◽  
Polynomial Fitting ◽  
Intelligent Monitoring ◽  
Hu Moment

Behavior analysis is the advanced stage in intelligence surveillance. In this paper, we first parameterize the scene knowledge using Hough Transform and Polynomial fitting to boundary of road. The algorithm of Self-Adaptive Background Subtraction was cited in order to segment the moving objects; the features of improved Hu moment were used for classification; and the cordon was cited to realize the behavior analysis of moving objects. The experimental results show that our algorithm is effective.

A Real Implementation of Dangerous Objects Capturing in Surveillance Web

2013 ◽  
Vol 278-280 ◽  
pp. 1032-1035
Author(s):  
Yun Cheng ◽  
Hai Tao Lang ◽  
Peng Yao ◽  
Rui Guo ◽  
Jian Ying Hu ◽  
...  
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Texture Feature ◽  
Multiple Scale ◽  
Experimental Results ◽  
Phase Method ◽  
Two Phase ◽  
Surveillance Camera ◽  
Object Learning ◽  
Classification Framework

The main focus of our research is capturing dangerous objects when they appear under the surveillance camera again, while have performed a dangerous activities in other places. Our solution is a two-phase method, including object learning and capturing under the classification framework. The samples of objects and non-objects are collected to train a classifier with libSVM in object learning phase. In object capturing phase, all moving objects are detected by background subtraction, then are classified into dangerous or non-dangerous. To obtain a robust objects representation to illumination, scale, rotation etc. we fuse HSV space based color feature and multiple scale texture feature. The experimental results with real surveillance data validated the proposed method.

Fast Background Subtraction Based on GPU

2011 ◽  
Vol 341-342 ◽  
pp. 737-742
Author(s):  
Jian Ping Han ◽  
Xiao Yang Li ◽  
Da Xing Zhang ◽  
Bo Ting Geng
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Experimental Results ◽  
Surveillance Videos ◽  
Effectiveness And Efficiency ◽  
Asynchronous Execution

In this paper, a fast background subtraction algorithm using codebook model is presented to extract moving objects from surveillance videos. The time for stopped objects being absorbed into the background can be controlled to deal with different applications and have nothing to do with the complexity of the scene. We implement the algorithm on GPU using CUDA, and optimize the implementation using pinned memory and asynchronous execution techniques. Experimental results are provided to demonstrate the accuracy, effectiveness, and efficiency of the proposed algorithm.

scholarly journals Background Modelling using a Q-Tree Based Foreground Segmentation

2020 ◽  
Vol 21 (1) ◽  
pp. 17-31
Author(s):  
S Shahidha Banu ◽  
N Maheswari
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
False Negative ◽  
Region Of Interest ◽  
Intensity Variation ◽  
Experimental Results ◽  
Foreground Segmentation ◽  
Foreground Detection ◽  
Foreground Object ◽  
Background Modelling

Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only inthe intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects. The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.

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