A New Moving Human Detection Method in Color Video Image

2012 ◽  
Vol 229-231 ◽  
pp. 1166-1170
Author(s):  
Tia Nai Wu ◽  
Yun Rong Wu ◽  
Yun Yu Wu
Keyword(s):  
Background Subtraction ◽  
Detection Method ◽  
Main Idea ◽  
Color Difference ◽  
Human Detection ◽  
Background Model ◽  
Video Frame ◽  
Difference Model ◽  
Background Updating ◽  
Background Image

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.

Application of Object-Based Image Processing Technique in Printing Defect Online Detection

2015 ◽  
Vol 731 ◽  
pp. 210-213
Author(s):  
Shu Feng Liu ◽  
Shao Hong Shen
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Main Idea ◽  
Color Difference ◽  
Processing Technique ◽  
Experimental Results ◽  
Image Processing Technique ◽  
Online Detection ◽  
Multi Scale ◽  
Color Printing

In this paper ,a color printing defect automatic online detection method based on digital image processing technique is proposed. The main idea of this method is comparison of defect product and template and it makes up of following key models. Firstly, multi-scale segmentation is applied to composed image which is overlaid by detecting product and template image. Secondly, an automatic region similarity analysis calculation is taken to segmentation obtained in multi-scale segmentation. The color difference between detecting product and template can be calculated accurately. Thirdly, defect detection results can be obtained according to threshold segmentation. Finally, the characteristics and advantages are approved by experimental analysis and discussion. Algorithm parameters are adjusted and modified to improve the stability and effectiveness. Experimental results approve that color printing defect automatic detection method in this paper has the characteristics of effectiveness and applicability. And experimental results indicate that this method has the advantage of judging the defect types automatically.

scholarly journals Penerapan Metode Background Subtraction dengan Menggunakan Kandidat Sampling Background untuk Deteksi Kemacetan

2018 ◽  
Vol 5 (6) ◽  
pp. 731
Author(s):  
Cipto Prabowo ◽  
Zurnawita Zurnawita
Keyword(s):  
Information Technology ◽  
Background Subtraction ◽  
Traffic Congestion ◽  
Background Model ◽  
Raspberry Pi ◽  
Model Research ◽  
Background Traffic ◽  
Web Camera ◽  
Model Determination ◽  
Background Image

<p class="Abstrak"><span lang="IN">Penggunaan teknologi informasi untuk mengatasi kemacetan lalu lintas sudah banyak digunakan, salah satunya adalah dengan menggunakan CCTV. Citra dari CCTV diurai antara <em>foreground</em> (citra kendaraan, sebagai salah satu komponen penyebab kemacetan) dan <em>background</em>nya (citra latar lalu lintas), fokus penelitian salah satunya adalah cara menentukan <em>background model</em> dengan berbagai macam teknik. Pada penelitian ini mencoba memberikan alternatif penentuan <em>background model</em> dengan menerapkan kandidat <em>sampling background</em> sebagai <em>background model. </em>Lokasi penelitan pada simpang bypass ketaping padang dan pengambilan gambar menggunakan raspberry pi dan web camera dengan arah tembakan menyamping dan durasi tembakan tiap menit. Hasil yang didapatkan cukup memuaskan terutama pada kondisi pagi dan siang hari.</span></p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"> </p><p class="Abstrak"><em><span lang="IN">The use of information technology to overcome traffic congestion has been widely used, one of them is by using CCTV. The image of CCTV is parsed between the foreground (vehicle image, as one of the components of the congestion cause) and the background (traffic background image), the focus of research is one way of determining the background model with various techniques. In this research try to give alternative background model determination by applying background sampling candidate as background model. Research location at intersection bypass ketaping and taking pictures using raspberry pi and web camera with sideways shot direction and duration of shots every minute. The results obtained are quite satisfactory, especially in the condition of morning and afternoon.</span></em></p>

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%.

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.

scholarly journals Strip-pair comparison method for building threshold color-difference model: theoretical model validation

2020 ◽  
Vol 28 (14) ◽  
pp. 21336
Author(s):  
Fernando Brusola ◽  
Ignacio Tortajada ◽  
Ismael Lengua ◽  
Begoña Jordá ◽  
Guillermo Peris-Fajarnés
Keyword(s):  
Theoretical Model ◽  
Model Validation ◽  
Color Difference ◽  
Comparison Method ◽  
Difference Model ◽  
Pair Comparison

Construction of Shadow Model by Robust Features to Illumination Changes

2013 ◽  
Vol 1 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Shuya Ishida ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
M. K. Bhuyan ◽  
Robert J. Woodham
Keyword(s):  
Detection Method ◽  
Harmful Effect ◽  
Color Space ◽  
Shadow Detection ◽  
Edge Information ◽  
Correlation Image ◽  
Illumination Changes ◽  
Sign Correlation ◽  
The Difference ◽  
Background Image

Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.

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.

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