用於監視攝影畫面的物體偵測演算法

林柏維

doi:10.6342/NTU201801479

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用於監視攝影畫面的物體偵測演算法

An Object Detection Algorithm for Video Surveillance

林柏維 , Masters　　Advisor：陳柏華　　

英文 DOI： 10.6342/NTU201801479

電腦視覺；機器學習；影像處理；物體偵測； Computer Vision ； Machine Learning ； Image Processing ； Object Detection

Abstract │ Reference (43) │ Altmetrics

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In recent years, object detection related algorithms mainly focus on im- proving detection accuracy as the main objective. They also focused on more accurately detecting the position of an object, and increasing the number of object classes. However, when improving the detection accuracy, the algo- rithms are becoming more complicated and requires more computing power. Some of them can only be implemented on high-end pieces of equipment. Therefore, they may not always be possible for deployment in real world applications. Besides, a great amount of the videos for object detection are stationed with a fixed angle, which means most pixels stay still between frames. In this case, there is no need for re-detection of the entire image. The purpose of this study is to improve the object detection efficiency for the fixed-angle camera and analyze the tradeoff of detection efficiency and accuracy. There are two main parts for improving the detection efficiency. One is reducing the unnecessary number of Region of Interest (ROI) for the image classification. T he other part is reducing the classification time through analyzing how the image feature descriptors affect the classification time and accuracy.
For the part of reducing unnecessary RoI, this research first applies the Gaussian Mixture Model (GMM) to subtract the background and then linear resize the foreground mask image to reduce the noise points. Then we use Features from Accelerated Segment Test (FAST) to detect the edges of each foreground part as feature points. Finally, we use these points to generate our ROIs. Comparing to the traditional sliding window method which could gen- erate about hundreds of thousands of ROI, and Selective Search which will generate about 2000 ROI, the method of this study will only generate ROI within a hundred, reducing the irrelevant ROIs. In addition, The window size and the aspect ratio are not constraints in our approach.
In this study AdaBoost was used as the classifier and the combinations of image gradient, LUV color space, and HOG as the feature descriptors. We used three image dataset as the testing data. The result showed that the classi- fication time can be reduced to 17% while the precision only decreased from 98.6% to 95.05%. We also propose the table of the different classification time with its corresponding precision.
For the whole detection process, the time ratio of each processing part is proposed. The algorithm we proposed can run average 160 FPS using only CPU with OpenCL acceleration, and 60 FPS without OpenCL acceleration. The result of this study can serve as a reference for object detection research which needs to satisfy certain precision or computing time constraint.

Reference ( 43 ) 〈TOP〉

[1] Song Bai, Xiang Bai, and Qi Tian. Scalable person re-identification on supervised smoothed manifold. In CVPR, volume 6, page 7, 2017.
[2] Sławomir Bak and Peter Carr. One-shot metric learning for person re-identification. In IEEE Conference on Computer Vision and Pattern Recognition, 2017.
[3] Rodrigo Benenson, Markus Mathias, Radu Timofte, and Luc Van Gool. Pedestrian detection at 100 frames per second. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2903–2910. IEEE, 2012.
[4] G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.
[5] Leo Breiman. Random forests. Mach. Learn., 45(1):5–32, October 2001. ISSN 0885-6125. . URL https://doi.org/10.1023/A:1010933404324.

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