A video-based real-time vehicle counting system using adaptive background method

2008 IEEE International Conference on Signal Image Technology and Internet Based Systems

組員：李瑋育，林立成，薩如鳴指導教授：吳宗憲 授課教授：連震杰1

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Outline Introduction Adaptive Background Estimation Segmentation

Background subtraction Shadow elimination

Counting Experimental Results Conclusions Demonstration

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Introduction Real time vehicle detection and counting system Two main methods

The adaptive background estimation Gaussian shadow elimination

Detector Inductive Loop Infra Red Radar Video based

More traffic information obtained Easily installed Scalable with progress in image processing techniques

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Introduction cont. Two goals

Robustness Self-adaptive to variant scenes (daytime, nighttime, overcast, shadow,

ghost, wind) Performance

Low cost equipment of algorithm needed so that processing time can be reduced under a required time

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System Overview

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Adaptive Background Estimation RGB 24 bits format video stream

luminance values of each pixel at time t of image

and absolute difference between gained defined result of experiment binaryzation operation between and Due to sensor noise from camera and light fluctuation

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Adaptive Background Estimation cont. Q1: Why fig.(b) highlights the different edge instead

of whole object? A:Overlapping between two different moving frames.

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Adaptive Background Estimation cont.

learning rate and controls the background speeds

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Adaptive Background Estimation cont. Fig.(d) is the current image Fig.(e) is the conjoined highlight pixel for each object

with the ROI mask Fig.(f) is the background model updated with the ROI

mask

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Background subtraction A robust background model is needed for segment

each frame into foreground and background objects

result of background subtraction , an absolute difference between and the

background model

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Background subtraction cont.

, and are automatic thresholds for each channel and evaluated by the background-subtracted image

Q2:Simple binaryzation isn’t sufficient to obtain a clear foreground so what should we do?

A:Morphological closing to fills the missing foreground pixels and morphological opening to remove the small isolated foreground pixels.

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Shadow elimination Foreground images contains

Moving objects Shadows

May cause erroneous in vehicle counting In the saturation channel shadow’s saturation is nearing road’s

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Shadow elimination cont. Saturation of background model has a Gaussian

distribution X:color Y:number of pixels Band-stop filter to remove shadow

saturation of background model

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Counting Count and register vehicle for each lane

“virtual detector” which were the rectangle region FGI include only moving objects

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Experimental Results Different hours in the day with fixed camera and resolution set

at 320x240 Written on C++ and executed on standard PC Scene 1: daytime, obvious shadows, camera faces the headlight Scene 2: daytime, cloudy day, non obvious shadows, camera faces the

tail-light Scene 3: daytime, cloudy day, non obvious shadows, camera faces the

headlight Scene 4: nighttime, non street lamp, camera faces the headlight

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Experimental Results cont. Q3:Why camera faces the light leads a better results? A: In the case of head-light heading , we can have

better segmentation due to the better difference amid the pixels of foreground object and the background , hereby we can have the better result in the shadow elimination output.

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Conclusions Detect and count vehicles in complex scenes Resolves relatively well various troublesome situation

Shadows Not able to recognize vehicle types

Lorry-driver Car Motorcycle

Implementing vehicle classification for improving the statistic function

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Demonstration Binary motion 1.avi

Binary motion mask computed using the frame differencing algorithm

Adaptive Background2.avi Background model updated with the binary motion mask

Adaptive Background with rectangle region3.avi Background model updated with the ROI mask

Adaptive Background Compare4.avi 比較利用 ROI mask和沒有利用 ROI mask的差別，可以明顯的看到利用 ROI

mask所更新的背景影像會比沒有利用 ROI mask來的清楚，因為可以整齊的清除掉殘留的物體移動軌跡。

Background subtraction5.avi 利用 ROI mask所得到的背景影像作 Background subtraction，可以清楚的取出移動物體。