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

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. Outline. Introduction Adaptive Background Estimation Segmentation

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

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  1. 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

  2. Outline • Introduction • Adaptive Background Estimation • Segmentation • Background subtraction • Shadow elimination • Counting • Experimental Results • Conclusions • Demonstration

  3. 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

  4. 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

  5. System Overview

  6. 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

  7. Adaptive Background Estimation cont. • Q1: Why fig.(b) highlights the different edge instead of whole object? • A:Overlapping between two different moving frames.

  8. Adaptive Background Estimation cont. • learning rate and controls the background speeds

  9. 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

  10. 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

  11. 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.

  12. 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

  13. 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

  14. Counting • Count and register vehicle for each lane • “virtual detector” which were the rectangle region • FGI include only moving objects

  15. 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

  16. 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.

  17. 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

  18. 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。

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