moving object detection based on multiple quadrant histogram ver1a
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Contents
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Introduction Moving Object Detection
Project Abstract
Development and Target System
Reference Paper Detection Algorithm
Project Flow chart
Development System
Event Trigger
Extracting Object Information
Applications
Future Work
Conclusions
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Introduction Moving Object Detection
Why Moving Object Detection
The core of security and business intelligence surveillance video application is the Moving
Object Detection. The information derived from the process can be used to alert security
camera monitoring staff to potential trespass or rule violation in the sensitive areas. In case of
business intelligence the information can be used to gather positive statistical information
relating to customer behavior.
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Project Abstract
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Problem Statement - Low Processing Power, Background less Moving Object Detection
As the complexity of the video analytics processing increases in embedded system, more will be theusage of efficient methods to detect the moving objects. Most of the video surveillance method use background model and the correctness of the surveillance depends on the background and adaptiveupdate to background. However this method take sufficient processing bandwidth and embedding into low cost solution becomes not feasible.
Alternately Histogram based detection is fairly efficient method to detect moving object. This method
can be combined with multiple quadrant event trigger to quickly detect object.
So this project aims to detect the moving object using technique which does not require backgroundmodel and derive a effective mechanism which can be embedded in ARM Board
Development Platform: MATLAB
Target Platform : TI ARM Processor
OS : Linux
Activity : Image processing Algorithm
Application : Video Surveillance
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Development and Target System
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Camera PC with MATLABUSB
Image acquisition
Algorithm development
Camera ARM ProcessorUSB
SDRAM NAND Flash
RS232 JTAG
Ethernet
PCwith MATLAB
Microsoft Webcam
30 frames/minute
Development System Target System
Hardware Platform
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Reference Paper Detection Algorithm
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Based on single Gaussian Distribution The color of every pixels are shown by Gaussian
distribution model Color and covariance differ, then pixel is
considered as foreground pixel After foreground detection, use average value and
covariance matrix of Gaussian to update Use inter-frame difference method and find the
difference between two adjacent frames in video
sequence Update background model Obtain moving object by processing difference
between current frame and updated background
model
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Project Flow chart
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PAD/Quadrant
FourQuadrant
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Project Flow chart
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Development System
Development System
Beagle Board - xM
LaptopMATLABWebcam
Status
S.No Activities Status
1 MATLAB Setup Completed
2 Study of Image ProcessingCommands
Completed
3 Image Acquisition using MATLABImage Acquisition Tool Box
Completed
4 Displaying Video Frames in MATLAB Completed
Output
File Traffic.avi
Path C:\Users\srikanthm\Desktop
Frame Width andheight
160 &120
No of Frames 61
The implementation of the moving object identification is planned in two phases. In Phase 1, themoving object detection algorithm is developed in MATLAB.In Phase 2, this algorithm is ported totarget Platform.
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Event TriggerThe implementation of the Event Trigger is done using MATLAB Program. The detector is a Padwhich covers an image region with dimensions 40x40 with starting Co-ordinate 40x60
The first step is construction of the threshold pad temporal difference image Dt
WherePt Pad image at time t
- Pixel Difference Threshold
For the traffic.avi file, the pad temporal difference image is constructed between frame1 and frame 61where the difference exists
The mean difference across the pad is calculated
Where N- is the number of pixels in the pad imagedt- mean differences threshold
The trigger occurs when rises through a mean difference and the mean differences areincreasing.
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Extracting Object Information
In absence of a trigger, a normalized 8 bin probably background grey scale histogram of the padimage at time t. The histogram is used to recursively update a time averaged probably background histogram as given below
Where
- pad image histogram typically of the order 0.2
Frame 1
t0
Frame 2
t1
Frame 31
t30
Frame 32
t31
Frame 60
t59
Frame 61
t60
PadObject
Event Trigger
AverageProbably backgroundHistogram
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Difference Histogram
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Frame 1t0
Frame 2t1
Frame 31t30
Frame 32t31
Frame 60t59
Frame 61t60
Pad
Object
Event Trigger
AverageProbably backgroundHistogram
Difference
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Difference Masked Pad Image Mp
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Frame 1t0
Frame 2t1
Frame 31t30
Frame 32t31
Frame 60t59
Frame 61t60
PadObjectPt(x,y)
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Dt(x,y)
x
Mp(x,y)
This contains objects, shadows, ghosts and zero values associated with static region
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Difference Masked Pad Image Mp
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Mp(x,y)
U=1
U=2
U=8
Dirac function ofCompare gray value
with bin boundary
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U=1
U=2
U=8
U=1
U=2
U=8
+ + + +
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Applications
Video Surveillance
No processing time waste in updating background Reality and Visual Effects
Medical Imaging
the security monitoring domain
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Future Work
A multiple pad can make a tripwire type arrangement which can be
effectively used for Tracker initialization. The existing method can beenhanced with additional tracker initialization and be used effectively insecurity alerts.
This embedded camera system may include short range wireless alertmessage features which can alert people around radius of 100m. Thefuture scope includes making a independent, smart video analytics deviceembedded in to camera itself which has facility to communicate tosurrounding intelligent devices too.
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Conclusions
We have presented a computationally economic approach to detection of
event as well as object detection and identification. It is one of a numberof approaches developed to use spare processing capacity for embeddedanalytics in intelligent cameras. This method significant differs from othermethods by efficient way of event trigger and histogram based detection.
It has potential for development in contexts such as Railway Platformdensity indication, or as a object identification in situations such as remotevideo surveillance. The method does not demand the development of afull background image or classifier training. It works with moderatequality monochrome footage and can be used in a range of contexts .
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Thank You
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