1. Moving Object Extraction Team 12
Team Members
Hari Kishan Bandaru ,
Sneha Anand Yeluguri,
V S P V S K Kumar Parimi

2. Overview Moving Object Detection Problem Classification
The Basic Methods
Conclusion
References

3. Moving Object Detection Problem
Detecting Moving objects from a video sequence of either a fixed or a moving camera.
Static Camera
Moving Camera
At present there exist three methods to detect moving targets,
Optical flow method
Consecutive Frames Subtraction
Background Subtraction

4. Contd… Applications : Video surveillance systems, Traffic monitoring,
Human motion capture,
Aerial and Ground sensors,
Situational Awareness,
Day or night operations,
Detection and tracking while moving,
Border protection and monitoring,
intelligent transportation,
intrusion surveillance, airport safety

5. Classification – Based on camera
Moving Object Detection using static camera
Detecting moving objects from a video sequence of a static camera.
Background – static
Foreground – moving objects
Moving Object Detection using moving camera
Find good feature to track
Track features
Classify foreground and background feature points:
Optical flow
Moving direction of feature
Length of moving direction
Decide region of foreground object

6. Contd… Affine motion model for background registration: <image>
The affine model describes the vector at each point in the image.
Need to find values for the parameters that best fit the motion present
Point feature tracker for correspondence between frame pairs
Iterative reweighted least squares to avoid the features in moving objects

7. Classification of methods
Optical flow method :
complex and bad anti-noise performance
can not be applied to real-time processing without special hardware device.
Consecutive Frames Subtraction :
Is a simple operation, realizes easily and has strong adaptability on the dynamic changes in the environment.
can not be completely extracted moving targets

8. Classification of methods
Background Subtraction :
Detect the moving objects as the difference between the current frame and the image of the scene background.
To detect moving objects, each incoming frame is compared with the background model learned from the previous frames to divide the scene into foreground and background.
It is widely used in many surveillance systems.
Advantage : does not require previous knowledge of moving objects such as shapes or movements.
Disadvantage : cannot discriminate moving objects from backgrounds when these backgrounds change significantly.

9. Classification of methods
Background images must adapt to :
Illumination changes, Distraction motions ( camera shake, moving tree, ocean waves, etc), shadows, bad weathers, etc.
Background Subtraction methods
Basic BGS
Running Gaussian average
Mixture of Gaussians
Enhanced mixture of Gaussians
Kernel Density Estimators
Mean-shift based estimation
Combined estimation and propagation
Eigen backgrounds

10. Classification of methods
These methods could be roughly divided into two approaches.
Methods of the first approach generate background models of each pixel, which is the minimum component of an image, using statistical distribution based on backward observation
Methods of the second approach generate background models of each small patch in images using features robust to changes in luminance.
There is a method for generating a background model which uses a combination of a pixel-wise background model and a patch- wise background model

11. Classification of methods
Basic BGS :
Pixels belongs to foreground if
| Current Frame – BG Image| > Threshold
BG image can be just the previous frame or the average image of a number of frames
Works only in particular conditions of objects’ speed and frame rate
Very sensitive to the threshold
At each new frame , each pixel is classified as either forground or background .
If the pixel is classified as foreground , it is ignored in the background model.

12. Classification of methods
Basic BGS – Example :

13. Classification of methods
Basic BGS limitations :
They do not provide an explicit method to choose the threshold.
Based on a single value , they cannot cope with multiple modal background distributions.

14. Classification of methods
Mixture of Gaussians
is the way to cope with multi modal background distributions.
Mixture of Gaussians actually models both the foreground and the background.
Region based mixture of Gaussians is one of the best methods for Moving Object Detection with Distraction Motions.

15. Consecutive Frames Subtraction Background Subtraction
Classification
Optical Flow method
Consecutive Frames Subtraction
Background Subtraction
Automatic moving Object Extraction using X-Means Clustering
Efficient Spatio-temporal segmentation for extracting moving objects in video sequences
Moving Object Detection with Background Model based on Spatio-Temporal Texture
Complex and bad anti-noise performance
cannot be applied to real-time processing without special
hardware device
simple operation, realizes easily
has strong adaptability on the dynamic changes in the environment
provides a moving object comprehensive and reliable Information
very sensitive to the irradiation which is caused by dynamic scene changes
Advantage of not requiring previous knowledge of moving objects such as shapes or movements
Disadvantage :cannot discriminate moving objects from backgrounds when these backgrounds change significantly

16. Background Subtraction
Classification
Background Subtraction
Generate background models of each pixel
Generate background models of each small patch in images using features robust to changes in luminance
Generate a background model using combination of a pixel-wise and a patch-wise background model
Advantage : These pixel-wise background models have an advantage of covering frequent changes in the pixel grayscale in local areas in images, such as waving of tree branches or fluctuations of water.
Advantage : have an advantage in that features in small patches represent textures robustly within the patch and they can cover global changes in images such as transitions of sunlight or adjustments of ceiling light
luminance.
Advantage : reduces false positives using conjunction of detection results of a pixel-wise method and a patch-wise method
Disadvantage :have a defect in that they hardly follow rapid translations of a pixel grayscale well across a global area in images, such as transitions of sunlight or adjustments of ceiling light luminance, because statistical distributions of each pixel grayscale belonging to the background translate substantially in a short time according to that kind of changes.
Disadvantage :have a defect in that they hardly follow rapid and intensive changes within image patches such as waving of tree branches or fluctuations of water, because textures within an image patch changes substantially according to that kind of changes
Disadvantage : this combinatorial method has a redundancy in that it executes two procedures of signal processes for one region in images.

17. Classification of methods
“AUTOMATIC MOVING OBJECT EXTRACTION USING X-MEANS CLUSTERING” paper deals with moving object detection and extraction and is an example for optical flow method.
A moving object extraction method based on region merging and that can automatically determine the number of extracted objects has been proposed
Steps in Moving Object Extraction Using X-Means Clustering
Region Segmentation by the Watershed Algorithm
Feature Point Selection and Motion Estimation
X-means Clustering and Region Labeling
Once the feature points are selected, affine motion parameter for each feature point is estimated.
Feature points are clustered by X-Means clustering for estimated affine motion parameters.
Finally , a label is assigned to the segmented region obtained by the morphological watershed algorithm.
The label is decided by voting for the feature point cluster in each region. Labeling result represent moving object extraction.

18. Classification of methods
“Efficient Spatio-temporal segmentation for extracting moving objects in video sequences”, this paper addresses a scheme to extract moving object from video sequences using the Consecutive frame subtraction method.
Here Each frame is decomposed into blocks
By taking the difference of two consecutive block images ,gives the binary mask which determines the moving blocks.

19. Classification of methods
“Moving Object Detection with Background Model based on Spatio- Temporal Texture” addresses a method for detecting moving objects with a background model that covers dynamic changes in backgrounds using a spatio-temporal texture which describes motion in addition to appearance.
This proposed method can cover global changes in images by using appearance information similar to other conventional spatial textures.
In addition, it can cover local changes in images by using motion information, although local changes are difficult to cover for conventional patch-wise models which use only appearance information.

20. Conclusion
We have tried to classify various methods for moving object detection and extraction.
Each classification has its own advantages and disadvantages and can be used based on various conditions and scenarios.

21. References
Imamura.K, Kubo.N, Hashimoto.H,"Automatic moving object extraction using x-means clustering Picture Coding Symposium (PCS),pp , Dec 2010.
Li, Qing-Zhong, He, Dong-Xiao, Wang, Bing "Effective Moving Objects Detection Based on Clustering Background Model for Video Surveillance“ Image and Signal Processing,vol.3,pp ,2008
Wenming Yang; Jilin Liu; Huahua Chen "Automatic extraction of moving objects in video sequences based on Spatio-temporal information“ Annual Conference of IEEE on Industrial Electronics Society on pp. 4,2005
Ren Ming-yi; Li Xiao-feng; Li Zai-ming,"Moving objects extraction from video sequences based on GMM and watershed“ International Conference on Communications, Circuits and Systems,pp ,2009
R. Li, S. Yu, and X. Yang, "Efficient spatio-temporal segmentation for extract ing moving objects in video sequences," IEEE Transactions on Consumer Electronics, vol. 54, pp , Mar
Kavitha, G.; Chandra, M.D.; Shanmugan, J."Video object extraction using model matching technique: a novel approach“ 14th International Workshop on Multimedia Communications and Services,pp ,2007

22. References
Yumiba, R.; Miyoshi, M.; Fujiyoshi, H."Moving object detection with background model based on spatio-temporal texture " IEEE Workshop onApplications of Computer Vision (WACV), pp ,2011.
Xiaoyan Zhang; Yong Shan; Wei Wei; Zijian Zhu,"An Image Segmentation Method Based on Improved Watershed Algorithm“ International Conference on Computational and Information Sciences (ICCIS),pp ,2010
Dewan, M.; Hossain, M.J.; Chae, O."Segmentation of moving object for content based applications",International Conference on Consumer Electronics,pp.1-2,2009
Mofaddel, M.A.; Abd-Elhafiez, W.M.,"Fast and accurate approaches for image and moving object segmentation",International Conference on Computer Engineering & Systems,pp ,2011
Xuehua Song; Jingzhu Chen; Chong He; Xiang Zhou,"A Robust Moving Objects Detection Based on Improved Gaussian Mixture Model“ International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol.2,pp ,2010
Xiong Weihua; Xiang Lei; Li Junfeng; Zhao Xinlong "Moving object detection algorithm based on background subtraction ",30th Chinese on Control Conference,pp ,2011