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.

An effective foreground segmentation using adaptive region based background modelling

2020 ◽  
Vol 48 (1) ◽  
pp. 23-34
Author(s):  
Shahidha Banu S. ◽  
Maheswari N.
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Background Subtraction ◽  
Moving Object ◽  
Foreground Segmentation ◽  
Foreground Object ◽  
Foreground Extraction ◽  
Content Type ◽  
Background Modelling ◽  
Modelling Method

Purpose Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during video analysis and surveillance in many real-time applications. It is usually done by background subtraction. This method is uprightly based on a mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it. Usually, this image is taken as the background model and is compared against every new frame of the input video sequence. In this paper, the authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algorithm reducing and raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction approaches. Moreover, the efficiency of the authors’ method is analyzed in different situations to prove that this method is available for real-time videos as well as videos available in the 2014 challenge change detection data set. Design/methodology/approach In this paper, the authors presented a fresh background modelling method for foreground segmentation. The main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object. Findings Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehensive view of research impact. Originality/value The algorithm used in the work was proposed by the authors and are used for experimental evaluations.

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.

scholarly journals Efficient FPGA Implementation of Human Detection from Video Sequences

2020 ◽  
Vol 9 (6) ◽  
pp. 242-248
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Median Filter ◽  
Region Of Interest ◽  
Video Stream ◽  
Human Detection ◽  
Video Sequences ◽  
Computer Vision Applications ◽  
And Performance ◽  
Better Than

Detection of Human is a vital and difficult task in computer vision applications like a police investigation, vehicle tracking, and human following. Human detection in video stream is very important in public security management. In such security related cases detecting an object in the video, sequences are very important to understand the behavior of moving objects which normally used in the background subtraction technique. The input data is preprocessed using a modified median filter and Haar transform. The region of interest is extracted using a background subtraction algorithm with remaining spikes removed using threshold technique. The proposed architecture is coded using standard VHDL language and performance is checked in the Spartan-6 FPGA board. The comparison result shows that the proposed architecture is better than the existing method in both hardware and image quality

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 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.

scholarly journals Saliency Detection with Moving Camera via Background Model Completion

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8374
Author(s):  
Yupei Zhang ◽  
Kwok-Leung Chan
Keyword(s):  
Deep Learning ◽  
Background Subtraction ◽  
Moving Objects ◽  
Saliency Detection ◽  
False Negative ◽  
Background Model ◽  
Dynamic Background ◽  
Video Completion ◽  
Model Completion ◽  
Background Image

Detecting saliency in videos is a fundamental step in many computer vision systems. Saliency is the significant target(s) in the video. The object of interest is further analyzed for high-level applications. The segregation of saliency and the background can be made if they exhibit different visual cues. Therefore, saliency detection is often formulated as background subtraction. However, saliency detection is challenging. For instance, dynamic background can result in false positive errors. In another scenario, camouflage will result in false negative errors. With moving cameras, the captured scenes are even more complicated to handle. We propose a new framework, called saliency detection via background model completion (SD-BMC), that comprises a background modeler and a deep learning background/foreground segmentation network. The background modeler generates an initial clean background image from a short image sequence. Based on the idea of video completion, a good background frame can be synthesized with the co-existence of changing background and moving objects. We adopt the background/foreground segmenter, which was pre-trained with a specific video dataset. It can also detect saliency in unseen videos. The background modeler can adjust the background image dynamically when the background/foreground segmenter output deteriorates during processing a long video. To the best of our knowledge, our framework is the first one to adopt video completion for background modeling and saliency detection in videos captured by moving cameras. The F-measure results, obtained from the pan-tilt-zoom (PTZ) videos, show that our proposed framework outperforms some deep learning-based background subtraction models by 11% or more. With more challenging videos, our framework also outperforms many high-ranking background subtraction methods by more than 3%.

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.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

Algorithm of Color Detection for Moving Video Objects Based on Mode Matching

2012 ◽  
Vol 239-240 ◽  
pp. 1000-1003
Author(s):  
Zhao Quan Cai ◽  
Hui Hu ◽  
Tao Xu ◽  
Wei Luo ◽  
Yi Cheng He
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Color Matching ◽  
Human Beings ◽  
Current Image ◽  
Color Recognition ◽  
Recognition Ability ◽  
Color Identification ◽  
The Difference ◽  
Background Image

It is urgent to study how to effectively identify color of moving objects from the video in the information era. In this paper, we present the color identification methods for moving objects on fixed camera. One kind of the methods is background subtraction that recognizes the foreground objects by compare the difference of pixel luminance between the current image and the background image at the same coordinates. Another kind is based on the statistics of HSV color and color matching which makes the detection more similar to the color identification of the human beings. According to the experiment results, after the completion of the background modelling, our algorithm of background subtraction, statistics of the HSV color and the color matching have strong color recognition ability on the moving objects of video.

Sign in / Sign up

Export Citation Format

Share Document