基於經驗模態分解法於夜間之行人偵測; Pedestrian Detection at Night Using Empirical Mode Decomposition

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Title:	基於經驗模態分解法於夜間之行人偵測;Pedestrian Detection at Night Using Empirical Mode Decomposition
Authors:	沈霈嫻;Pei-Xian Shen
Contributors:	資訊工程研究所
Keywords:	經驗模態分解;高斯混合模型;Gaussian mixture model;Empirical mode decomposition
Date:	2009-07-20
Issue Date:	2009-09-22 11:57:51 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	夜間行人偵測系統往往因為監控系統當下的環境光線不夠亮，或是對比度不夠強烈，而造成監控時無法準確地抓出行人的移動軌跡。因此，在監視系統所取得的影像，其經過處理後的光線是否充足，對於偵測及追蹤系統之成效有很大的影響。對於周圍光線昏暗的停車場，或是無法即時由監控影片找出移動物軌跡的監控場所，此論文提出了一套系統，來進行事後的分析及處理。我們先將影片中的亮度利用經驗模態分解(Empirical Mode Decomposition)做調整，與一般的影像亮度強化調整方法不同的是，一般的調整方法雖可將過於曝光的部分調暗，但過於黑暗的地方卻也被調得更暗了。而經驗模態分解則可以將影像中過於曝光或黑暗的地方同時做適當地調整，使影像對比度提昇至可監控的條件，幫助警察正確的找出犯罪者的移動軌跡，推測可能犯罪的行蹤。在影像前處理結束後，我們使用高斯混合背景模型(Gaussian Mixture Model)來做行人的偵測及追蹤。最後，利用連通元件來標示偵測到的行人。相較於夜間監控系統使用的紅外線攝影機，本研究使用的是一般的數位攝影機，達到了以低成本器材進行於不良環境下的監控。實驗結果證明，經過經驗模態分解法的處理，可以讓過暗的影像藉由調整光線強度，使其成為亮度、對比度較為正常的影像，以達到在黑暗中辨識率提昇的地步。 Since the light in the night environment is neither bright enough nor the contrast is strong enough, the pedestrian detection system frequently fails in correctly tracking the people’s moving trajectories in most video surveillance systems. Hence, the influence of light plays an important role in deciding the success of a video tracking or surveillance system, especially at night. To cope with the problems occurring in most outdoor parking areas where the light is usually dim or the places where the trajectories of moving objects can not be successfully found, a video surveillance system aiming at night environments is proposed in this thesis. In the proposed system, an Empirical Mode Decomposition (EMD) method is first employed to adjust the luminance in the images. EMD is different from general image luminance adjusting method by merely strengthening and adjusting the places of over-exposure darker, which results in adjusting more darker in the darker places. Moreover, the EMD can suitably adjust both overexposure and over-dark images to enhance the contrast of images so as to find out the moving trajectory of suspects or criminals correctly. After the pre-processing conducted by EMD, Gaussian mixture model is then employed to perform the task of pedestrian detection and tracking. Finally, utilize connected component labeling technique to mark the detected pedestrian. Comparing with the surveillance systems that use infrared camera, our work merely uses ordinary digital cameras with low-cost to accomplish the same job. Experimental results demonstrate that our work through the processing of EMD can indeed uplift the detection and tracking performance of video surveillance at night.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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