遮蔽情況下之群眾人數統計

Counting Pedestrians in Crowds under Occlusions

林祐綱(You-Kang Lin)

指導教授 ： 謝君偉

元智大學/電機通訊學院/電機工程學系/碩士(2009年)

摘要

從遮蔽情況的群眾影像裡頭切割行人並統計人數一直以來都是影像處理上的一個難題，通常處理群眾人數統計問題的研究都會做一些假設以簡化問題。例如攝影機角度必須從人群正上拍攝，這樣的話，人群之間的遮蔽情況會被簡化成區塊合併與分開的問題，不會有深度上的近景遮蔽遠景的問題；另外也可能假設利用多隻攝影機共同監視一個區域，再利用多隻不同角度攝影機的多方面資訊彌補單隻攝影機資訊上的不足。但是這些假設不符合實際應用，因此本論文將針對一般監視器(單隻攝影機側面俯角拍攝)的視角做群眾人數統計。本論文使用輪廓比對做快速群眾人數估測，並利用馬可夫鏈蒙地卡羅法(Markov chain Monte Carlo Method)做後續樣板比對驗證實際人數，然後藉由樣板比對的結果動態產生新的輪廓模型。

關鍵字

人數統計 ； 重心描述元 ； 馬可夫鏈蒙地卡羅

並列摘要

It’s difficult to segment and count people in occlusion. This problem has been an important task for a long time. Usually, we propose some hypotheses to simplify the problem of counting people in crowded. For example, the camera must be set the higher place top of humans. Therefore, the occlusion event between people will be simplified to blob merge and split. The problem of occlusion which far objects cover with near objects will be not produced. On the other hand, we can use multi cameras to surveillance the area which has overlap region between cameras. Then, use different information of cameras to make up for the problem of information is not enough in single camera. But those supposes are not suitable in real work. So, this thesis will count people under normal camera (one camera in side-view). This thesis uses contour matching to complete people counting quickly. It also uses markov chain monte carlo method to do the follow-up procedure of template matching. Finally, we use the result of template matching to create new contour model on-line.

並列關鍵字

People Counting ； Centroid Context ； Markov chain Monte Carlo.

參考文獻

[1] V. Rabaud and S. Belongie, “Counting Crowded Moving Objects,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 705-711, Jun. 2006.

[2] G. Antonini and J.P. Thiran, “Counting Pedestrians in Video Sequences Using Trajectory Clustering,” IEEE Transactions On Circuits And Systems For Video Technology, Vol. 16, No. 8, pp. 1008-1020, August 2006.

[3] T. Zhao, R. Nevatia, and B. Wu, “Segmentation and Tracking of Multiple Humans in Crowded Environments,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 7, pp. 1198-1211, July 2008.

[5] Z. Lin, L.S. Davis, D. Doermann, and D. DeMenthon, “Hierarchical Part-Template Matching for Human Detection and Segmentation,” International Conference on Computer Vision, pp. 1-8, Oct. 2007.

[7] B. Wu and R. Nevatia, “Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors,” International Conference on Computer Vision, Vol. 1, pp. 90-97, Oct. 2005.