INTRODUCTION
Moving object detection finds lot of application surveillance and traffic monitoring. There are many methods for object detection. Object detection generally involves Key-Frame extraction and Background Subtraction. Key frame extraction summarizes video by eliminating transitional frames, thus reducing computational load. Frame difference, local binary differential, wavelet based and histogram based methods are some of the methodologies used to obtain key frames.
KEY FRAME EXTRACTION
The frames which contain all the important information of a video shot are called the key frames of that video shot. Current day, Video sequences that have high frame rate and high resolution. A video of frame rate 25fps and 1920X1080 grayscale image produces approximately 6MB of data every second. The amount of data to be processed can be reduced by choosing only the key frames from video shot. Key frames are set of frames that summarize a video shot. Applying image processing algorithms only on key frames reduces computational load with little loss in generality. A video sequence containing moving object has many transitional frames that have very minimum displacement when compared to its position in the previous frame.Key Frame Extraction techniques drop frames that do not show significant changes with respect to previous frames while maintain the essential information required for human eye to judge the direction and apparent change in motion of the object. Such techniques find application in video compression, content based video retrieval and video classification.
Most algorithms for key frame extraction involve finding inter frame distance and comparing it with threshold. Wavelet Transformation, local binary pattern (texture b...
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...e Hui, "An improved canny edge detection algorithm based on predisposal method for image corrupted by gaussian noise," World Automation Congress (WAC), 2010 , vol., no., pp.113,116, 19-23 Sept. 2010.
Caixia Deng; Weifeng Ma; Yin Yin, "An edge detection approach of image fusion based on improved Sobel operator," Image and Signal Processing (CISP), 2011 4th International Congress on , vol.3, no., pp.1189,1193, 15-17 Oct. 2011.
Fan Zhang; Lei Yang; Guangwen Zhang, "Adaptive fast Gaussian background subtraction algorithm," Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on , vol., no., pp.766,771, 29-31 Dec. 2012
Xiaochun Zou; Xinbo Zhao; Zheru Chi, "A robust background subtraction approach with a moving camera," Computing and Convergence Technology (ICCCT), 2012 7th International Conference on , vol., no., pp.1026,1029, 3-5 Dec. 2012
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
...ction for the tampering decision. Texture- and edge-based features are extracted from illuminant estimators which are then provided to a machine-learning approach for automatic decision-making. SVM is used for classification and detection rates of 86% on a dataset consisting of 200 images and 83% on 50 images collected from the Internet was achieved. Another detection scheme based on blur as a clue is proposed in [77]. This method expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations.
Watamaniuk, S. N. J., McKee, S. P., & Grzywacs, N. M. (1995). Detecting a Trajectory Embedded in Random-direction Motion Noise. Vision Research, 35(1), 65-77.
I initially turned to night vision, initially this sounds nice, but after some thought on the basic principles of how night vision works, I decided against it for several reason.
...Variational Image Thresholding N. Ray, B.N. Saha Alberta Univ., Edmonton Proceedings / ICIP ... International Conference on Image Processing 01/2007; 6:VI - 37 - VI - 40. DOI:10.1109/ICIP.2007.4379515 ISBN: 978-1-4244-1437-6 In proceeding of: Image Processing, 2007. ICIP 2007. IEEE International Conference on, Volume: 6
Video surveillance has existed in the work place since the invention of the television. Video surveillance cameras come in all shapes and sizes, from the obvious ones to some that are small as a dime. Many employers purchase such devices to capture employees and customers who commit theft and fraud, or any other illegal activity. The increase of technology in electronic monitoring is now able to transfer images from a camera to a computer where the employer is able to zoom in and make a positive identity of the employee or customer who is committing an illegal act.
After the initial pre processing steps of smoothening and removal of noise, the edge strength is calculated by taking the gradient of the image. For the purpose of edge detection in an image, the Sobel operator first performs a 2-D spatial gradient measurement with the help of convolution masks. The convolution masks used is of the size 3X3, where one is used to calculate the horizontal gradient(Gx) while the other is used to calculate the vertical gradient(Gy). Then, the approximate absolute edge strength can be calculated at each point. The masks used for the convolution process is as shown
Table 1: Retrieved from: Gray K, Obstacle detection sensor technology. In: Proceedings of the ASAE conference in automation technology for off-road equipment, Chicago, Illinois, USA; July 26–27, 2002. p. 442–50.,
Technology has become more important in this millennium era, as it has become a need for almost every people in the world. With the development of technology, the statistics of criminalization had also increases as there are many new methods to do their crime by misusing the technology that was invented to help people. Various technology inventions had been made to overcome this problem. One of them is a surveillance method that was invented to keep an eye on the criminal, be it an intruder, a robber or a theft. The objective of this project is to come up with a more advanced surveillance system than the previous one, can respond quickly (faster), have the ability to integrate different party, which is the owner, the security, and the police, and can inform the police and the user quickly by using SMS, MMS and email. Our project also aims to develop a system that offers more support than the previous system and have the ability to monitor and track multi-target criminal, whether it is in a fixed property (house) or moving property (car).
the propose of image block motion Extraction is to use 2D image of foreground and background which extract the predominant lines and vanishing point of the background base on geometry method and motion, the background depth map is considered. The depth of the object have used the motion extraction to obtain the motion depth map and then divided according to the color of the object to give the object region segmentation depth map.
In 3.1 sections, if the background colour has the same skin pixel with the human face, it will certainly become a problem in detecting human face. So in this step, we not only prepare eyes, nose and mouth data sets but also for the face to increase the accuracy in detecting face.
[1] Yoshihiro Sato, Jun Takamatsu, Hiroshi Kimura, and Katsushi Ikeuchi, "Recognition of a Mechanical Robust Object Tracking," Proc. of 2003 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems(MFI2003), pp.329-334, July 2003
Edge detection, a very popular method that extracts the obstacle vertical edges and drives the robot around either one of the visible edges.
Following figure show the gray level image. An image is used in many fields for example entertainment, remote sensing, medical image, etc. which is very useful for human life. In this thesis gray scale image is use for remove impulse noise.
Chen, Y.L.― Night time vehicle light detection on a moving vehicle using image segmentation and analysis techniques― WSEAS Trans.Comput. 8(3), 506–515 (2009)