Motion Object and Regional Detection Method Using Block-Based Background Difference Video Frames

2012 ◽  
Author(s):  
Jiwoong Bang ◽  
Daewon Kim ◽  
Hyeonsang Eom
Keyword(s):  
Detection Method ◽  
Video Frames ◽  
Block Based ◽  
Background Difference

scholarly journals Blotch Detection in Archive Films Based on Visual Saliency Map

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yildiz Aydin ◽  
Bekir Dizdaroğlu
Keyword(s):  
Computational Complexity ◽  
Cultural Heritage ◽  
False Alarm ◽  
Detection Method ◽  
Visual Saliency ◽  
Saliency Map ◽  
Saliency Maps ◽  
Block Based ◽  
The Given ◽  
Blotch Detection

Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. Here, a block-based blotch detection method is proposed based on a visual saliency map. The visual saliency map reveals prominent areas in an input frame and thus enables more accurate results in the blotch detection. A simple and effective visual saliency map method is taken into consideration in order to reduce computational complexity for the detection phase. After the visual saliency maps of the given frames are obtained, blotch regions are estimated by considered spatiotemporal patches—without the requirement for motion estimation—around the saliency pixels, which are subjected to a prethresholding process. Experimental results show that the proposed block-based blotch detection method provides a significant advantage with reducing false alarm rates over HOG feature (Yous and Serir, 2017), LBP feature (Yous and Serir, 2017), and regions-matching (Yous and Serir, 2016) methods presented in recent years.

A Research on Real-Time Hand-Detection Technology Oriented to the Somatic Games

2013 ◽  
Vol 748 ◽  
pp. 999-1002 ◽  
Author(s):  
Ren Chen ◽  
Hui Li
Keyword(s):  
Real Time ◽  
Failure Rate ◽  
Skin Color ◽  
Detection Rate ◽  
Detection Method ◽  
Hand Detection ◽  
Key Technology ◽  
Video Frames ◽  
Detection Technology ◽  
Adaboost Classifier

Hand-detection is a key technology to the somatic games. In this paper, we present a real-time hand-detection method based on Adaboost and skin-color characteristic. By processing the video frames with Adaboost classifier, we abstract the target regions which may contain the hand gestures. Then a filter based on skin color is proposed to select the correct regions. The best detection rate reaches above 89% with an acceptable failure rate and misjudgment rate. Experimental results show that this method is a lightweight and rapid approach to implement real-time hand detection in somatic games.

Adjacent-block based statistical detection method for self-embedding watermarking techniques

2009 ◽  
Vol 89 (8) ◽  
pp. 1557-1566 ◽  
Author(s):  
Hong-Jie He ◽  
Jia-Shu Zhang ◽  
Fan Chen
Keyword(s):  
Detection Method ◽  
Adjacent Block ◽  
Statistical Detection ◽  
Block Based

scholarly journals Performance Evaluation of Block based Video Watermarking after Attacks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 225-233
Keyword(s):  
Performance Evaluation ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Media ◽  
Video Watermarking ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Video Frames ◽  
Block Based

In today’s era use of digital media is most popular way of communication. Digital media covers images, videos and animations available online. The easy methods of accessing, copying and editing digital media have made them more popular. With several advantages these easy methods of copying and editing data have created some big issues like ownership identification. This increases the demand of protecting online digital media. Watermarking is solution of such problem. In this work, a block-based method has been proposed for video watermarking that uses a key at the time of embedding and extraction. Some frames are selected from the video according to a key. Watermark is embedded on the selected frames after dividing into parts called blocks. Each part of the watermark is embedded in one selected frame of the video. This method increases the security of the system as the complete watermark cannot be extracted without knowing the positions of watermarked frames and the position of the block in that frame. Watermarking is performed in the Discrete Wavelet Transform domain after scaling of watermark data. To show the authenticity of proposed scheme various attacks are applied on different watermarked video frames and extracted watermark results are shown under different tables.

Moving Shadow Detection Method Using Logistic Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2724-2727
Author(s):  
Liu Yang ◽  
Jiang Yan Dai ◽  
Miao Qi ◽  
Qing Ji Guan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Moving Objects ◽  
Detection Method ◽  
Experimental Results ◽  
Shadow Detection ◽  
Video Frames ◽  
Moving Shadow Detection

We present a novel moving shadow detection method using logistic regression in this paper. First, several types of features are extracted from pixels in foreground images. Second, the logistic regression model is constructed by random pixels selected from video frames. Finally, for a new frame in one video, we take advantage of the constructed regression model to implement the classification of moving shadows and objects. To verify the performance of the proposed method, we test it on several different surveillance scenes and compare it with some well-known methods. Extensive experimental results indicate that the proposed method not only can separate moving shadows from moving objects accurately, but also is superior to several existing methods.

scholarly journals Sperm detection in video frames of semen sample using morphology and effective ellipse detection method

2011 ◽  
Vol 1 (3) ◽  
pp. 8
Author(s):  
HS Mahdavi ◽  
A Monadjemi ◽  
A Vafae
Keyword(s):  
Detection Method ◽  
Semen Sample ◽  
Ellipse Detection ◽  
Video Frames ◽  
Sperm Detection

Object detection method with tracking compensation based on space-time background difference

2010 ◽  
Vol 30 (1) ◽  
pp. 58-60 ◽  
Author(s):  
Tao WANG ◽  
Yun-yu XIONG
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Space Time ◽  
Background Difference
Sign in / Sign up

Export Citation Format

Share Document