整合雙眼與單眼視覺技術的行人偵測

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姓名

吳怡君(Yi-chun Wu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

整合雙眼與單眼視覺技術的行人偵測
(Pedestrian Detection by Integrating Binocular and Monocular Vision Methods)

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摘要(中)

隨著經濟的成長，機動車輛愈來愈多，因而交通事故也愈來愈多。有鑑於此，發展車輛輔助安全駕駛的議題，也就愈顯示其重要性。市區的行人防撞是其中一項重要議題。在本研究中，我們提出整合雙眼與單眼視覺技術做行人偵測，並應用於車輛前方上，以避免己車撞及行人。
在車輛前方的行人偵測中，我們先將左右影像經由雙相機校正方法，依序透過相機參數校正、扭曲校正、以及影像矯正，得到左右極線彼此平行且對齊的影像。其中，經過影像矯正後的影像與光軸的交點已經改變，所以在此我們將影像矯正後的影像再做一次相機參數校正，得到目前影像與相機的新關係。接著進入視覺技術偵測，首先透過關聯式動態規劃法計算出像差圖；將像差圖經由形態學平滑化消除雜訊；利用v-像差將地面資訊濾除，接著產生連結區塊，將區塊根據在影像上的長度及距離分為長度太小與夠大兩類；長度太小的區塊則根據單眼影像的色彩資訊判斷相鄰且距離差距不大的區塊是否為同一物體，接著與長度夠大的區塊一起經由地面消失線濾除高於地面太多的物體，並判斷區塊的距離及長度是否符合我們定義的長度範圍；最後根據行人的長寬比例框出結果並根據像差值求得該物體與攝影機之距離。

摘要(英)

In these few decades, the vehicle number is rapidly increasing. In addition to the vehicle number, more factors of road situation, driving environment, and human attention result in a large amount of traffic accidents and casualties. Therefore, it is important to develop real-time automotive driver assistance systems. Pedestrian collision avoidance is one of the important issues. In this study, we propose integrating binocular and monocular vision methods for pedestrian detection, and apply in preceding vehicle detection to avoid collision.
In pedestrian detection, we use images of left and right cameras to obtain epipolar lines of left and right images by camera calibration and image rectification. We can obtain the refined images of left and right by aligning the epipolar lines. However, the optical axis of the refined images have been changed because of image rectification, we must obtain the relationship between new images and cameras by camera calibration. Then, we use associated dynamic programming algorithm to obtain disparity map from new images. We reduce noise of disparity map by morphology smoothing, and we filter out the ground by v-disparity. We divide the disparity map into many components by connected component algorithm. We can determine whether the component is pedestrian according to the rules of pedestrian determination and estimate the distance between camera and detected object.

關鍵字(中)

★ 影像矯正
★ 相機校正

關鍵字(英)

★ camera calibration
★ image rectification

論文目次

摘要 II
Abstract IV
誌謝 IV
目錄 V
圖表目錄 VII
表格目錄 IX
第一章緒論 1
1.1 研究動機 1
1.2 系統概述 1
1.3 論文架構 3
第二章相關研究 5
2.1 雙眼立體視覺對應方法 5
2.1.1 密集像差圖的雙眼立體視覺對應法 5
2.1.2 稀疏像差圖的雙眼立體視覺對應法 8
2.2 基於雙眼立體視覺技術的行人偵測方法 11
2.2.1 以雙眼立體視覺技術偵測感興趣區域 11
2.2.2 以雙眼立體視覺技術偵測行人 14
2.3 基於單眼立體視覺技術的行人偵測方法 14
2.3.1 以單眼視覺技術偵測感興趣區域 14
2.3.2 以單眼視覺技術偵測行人 16
第三章雙相機校正 22
3.1 相機參數校正 22
3.1.1 相機成像模型 22
3.1.2 相機參數校正方法 25
3.2 鏡頭扭曲校正 30
3.2.1 視野模型 31
3.2.2 估測扭曲參數 31
3.2.3 估計最佳解 32
3.3 影像矯正 33
3.3.1 極線幾何 33
3.3.2 如何利用極線幾何關係做影像矯正 37
3.4 第二次相機參數校正 40
第四章視覺偵測技術 41
4.1 雙眼立體視覺 41
4.1.1 深度推算 41
4.1.2 關聯式動態規劃法 43
4.2 行人偵測 47
4.2.1 去除雜訊 48
4.2.2 濾除地面資訊 50
4.2.3 區塊連結 51
4.2.4 結合深度資訊及單眼資訊 52
4.2.5 行人偵測篩選準則 52
第五章實驗 56
5.1 實驗設備與架設環境 56
5.2 雙相機校正 57
5.3 視覺偵測技術 60
5.4 距離估測準確度的比較 60
5.5 動態成果展示 65
第六章結論與未來展望 68
6.1 結論 68
6.2 未來展望 69
參考文獻 70

參考文獻

[1] Bertozzi, M., A. Broggi, M. D. Rose, A. Rakotomamonjy, and Suard, F., "A pedestrian detector using histograms of oriented gradients and a support vector machine classifier," in Proc. IEEE Conf. Intelligent Transportation Systems, Seattle, WA, Sep.30-Oct.3, 2007, pp.143-148.
[2] Birchfield, S., B. Natarajan, and C. Tomasi, "Correspondence as energy-based segmentation," Image and Vision Computing, vol.25, no.8, pp.1329-1340, 2007.
[3] Boykov, Y., O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.23, no.11, pp.1222-1239, 2001.
[4] Broggi, A., M. Bertozzi, A. Fascioli, and M. Sechi, "Shape-based pedestrian detection," in Proc. 4th IEEE Intelligent Vehicles Symposium, Dearborn, Michigan, Oct.3-5, 2000, pp.215-220.
[5] Chen, Z., C. Wu, and L. Tang, "Image rectification based on minimal epipolar distortion," in Proc. SPIE Conf. Vision Geometry X, San Diego, CA, Jul.29-30, 2001, pp.186-193.
[6] Cox, I. J., S. L. Hingorani, S. B. Rao, and B. M. Maggs, "A maximum likelihood stereo algorithm," Computer Vision and Image Understanding, vol.63, no.3, pp.542-567, 1996.
[7] Deng, Y. and X. Lin, "A fast line segment based dense stereo algorithm using tree dynamic programming," in Proc. 9th European Conf. Computer Vision, Graz, Austria, May 7-13, 2006, pp.201-212.
[8] Devernay, F. and O. Faugeras, "Straight lines have to be straight," Machine Vision and Applications, vol.13, no.1, pp.14-24, 2001.
[9] Fang, Y., I. Masaki, and B. Horn, "Depth-based target segmentation for intelligent vehicles fusion of radar and binocular stereo," IEEE Trans. Intelligent Transportation Systems, vol.3, no.3, pp.196-202, 2002.
[10] Gong, M. and Y. Yang, "Fast unambiguous stereo matching using reliability-based dynamic programming," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.27, no.6, pp.998-1003, 2005.
[11] Huh, k., J. Park, J. Hwang, and D. Hong, "A stereo vision-based obstacle detection system in vehicles," Optics and Lasers in Engineering, vol.46, no.2, pp.168-178, 2008.
[12] Kim, J.-C., K.-M. Lee, B.-T. Choi, and S.-U. Lee, "A dense stereo matching using two-pass dynamic programming with generalized ground control points," in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-25, 2005, pp.1075-1082.
[13] Klaus, A., M. Sormann, and K. Karner, "Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure," in Proc. 18th Int. Conf. Pattern Recognition, Hong Kong, Aug.20-24, 2006, pp.15-18.
[14] Kolmogorov, V. and R. Zabih, "Computing visual correspondence with occlusions using graph cuts," in Proc. 8th Int. Conf. Computer Vision, Vancouver, Canada, Jul.7-14, 2001, pp.508-515.
[15] Kolmogorov, V. and R. Zabih, "Multi-camera scene reconstruction via graph cuts," in Proc. 7th European Conf. Computer Vision, Copenhagen, Denmark, May 28-31, 2002, pp.82-96.
[16] Krotosky, S. J. and M. M. Trivedi, "On color-, infrared-, and multimodal-stereo approaches to pedestrian detection," IEEE Trans. Intelligent Transportation Systems, vol.8, no.4, pp.619-629, 2007.
[17] Krumm, J., S. Harris, B. Meyers, B. Brumitt, M. Hale, and S. Shafer, "Multi-camera multi-person tracking for easyliving," in Proc. 3rd IEEE Int. Workshop on Visual Surveillance, Dublin, Ireland, Jul.1, 2000, pp.3-10.
[18] Larsen, S., P. Mordohai, M. Pollefeys, and H. Fuchs., " Temporally consistent reconstruction from multiple video streams using enhanced belief propagation," in Proc. 11th Int. Conf. Computer Vision, Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp.1-8.
[19] Loop, C. and Z. Zhang, "Computing rectifying homographies for stereo vision," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Fort Collins, CO, Jun.23-25, 1999, pp.125-131.
[20] Marquardt, D., "An algorithm for least-squares estimation of nonlinear parameters," SIAM Journal on Applied Mathematics, vol.11, pp.431-441, 1963.
[21] Mattoccia, S., F. Tombari, and L. D. Stefano, "Stereo vision enabling precise border localization within a scanline optimization framework," in Proc. 8th Asian Conf. Computer Vision, Tokyo, Japan, Nov.18-22, 2007, pp.517-527.
[22] Min, D. and K. Sohn, "Cost aggregation and occlusion handling with WLS in stereo matching," IEEE Trans. Image Processing, vol.17, no.8, pp.1431-1442, 2008.
[23] Nedevschi, S., S. Bota, and C. Tomiuc, "Stereo-based pedestrian detection for collision-avoidance applications," IEEE Trans. Intelligent Transportation Systems, vol.10, no.3, pp.380-391, 2009.
[24] Papageorgiou, C. and T. Poggio, "A trainable system for object detection," Int. Journal of Computer Vision, vol.38, no.1, pp.15-33, 2000.
[25] Ran, Y., Q. Zheng, I. Weiss, L. S. Davis, W. Abd-Almageed, and L. Zhao, "Pedestrian classification from moving platforms using cyclic motion pattern," in Proc. IEEE Int. Conf. Image Processing, Genoa, Italy, Sep.11-14, 2005, pp.854-857.
[26] Scharstein, D. and R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," Int. Journal of Computer Vision, vol.47, no.1-3, pp.7-42, 2002.
[27] Stein, G. P., O. Mano, and A. Shashua, "A robust method for computing vehicle ego-motion," in Proc. 4th IEEE Intelligent Vehicles Symposium, Dearborn, MI, Oct.3-5, 2000, pp.362-368.
[28] Su, J., R. Chung, and L. Jin, "Homography-based partitioning of curved surface for stereo correspondence establishment," Pattern Recognition Letters, vol.28, no.12, pp.1459-1471, 2007.
[29] Suard, F., A. Rakotomamonjy, A. Bensrhair, and A. Broggi, "Pedestrian detection using infrared images and histograms of oriented gradients," in Proc. IEEE Intelligent Vehicles Symposium, Tokyo, Japan, Jun.13-15, 2006, pp.206-212.
[30] Tomiuc, C., S. Nedevschi, and M. M. Meinecke, "Pedestrian detection and classification based on 2D and 3D information for driving assistance systems," in Proc. IEEE Int. Conf. Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, Sep.6-8, 2007, pp.133-139.
[31] Toulminet, G., M. Bertozzi, S. Mousset, A. Bensrhair, and A. Broggi, "Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis," IEEE Trans. Image Processing, vol.15, no.8, pp.2364-2375, 2006.
[32] Veksler, O., "Stereo correspondence by dynamic programming on a tree," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, Jun.20-25, 2005, pp.384-390.
[33] Wang, H., Q. Chen, and W. Cai, "Shape-based pedestrian/bicyclist detection via onboard stereo vision," in Proc. IMACS Multiconf. Computational Engineering in Systems Applications, Beijing, China, Oct. 4-6, 2006, pp.1776-1780.
[34] Wang, Z.-F. and Z.-G. Zheng, "A region based stereo matching algorithm using cooperative optimization," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, Alaska, Jun.24-26, 2008, pp.1-8.
[35] Xu, Z., L. Ma, M. Kimachi, and M. Suwa, "Efficient contrast invariant stereo correspondence using dynamic programming with vertical constraint," Visual Computer, vol.24, no.1, pp.45-55, 2008.
[36] Yang, R. and M. Pollefeys, "Multi-resolution real-time stereo on commodity graphics hardware," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Madison, WI, Jun.18-20, 2003, pp.211-217.
[37] Yang, Q., L. Wang, R. Yang, S. Wang, M. Liao, and D. Nistér, "Real-time global stereo matching using hierarchical belief propagation," in Proc. 17th British Machine Vision Conf., Edinburgh, UK, Sep.4-7, 2006, pp.989-998.
[38] Yang, Q., L. Wang, R. Yang, H. Stewénius, and D. Nistér, "Stereo matching with color-weighted correlation, hierarchical belief propagation and occlusion handling," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.31, no.3, pp.492-504, 2009.
[39] Yu, T., R. Lin, B. Super, and B. Tang, "Efficient message representations for belief propagation," in Proc. 11th Int. Conf. Computer Vision, Rio de Janeiro, Brazil, Oct. 14-21, 2007, pp.1-8.
[40] Zhang, Z., "Determining the epipolar geometry and its uncertainty: A review," Int. Journal of Computer Vision, vol.27, no.2, pp.161-195, 1998.
[41] Zhang, Z., "A flexible new technique for camera calibration," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.11, pp.1330-1334, 2000.
[42] Zhao, L. and C. E. Thorpe, "Stereo- and neural network-based pedestrian detection," IEEE Trans. Intelligent Transportation Systems, vol.1, no.3, pp.148-154, 2000.
[43] Zhao, L., Dressed Human Modeling, Detection, and Parts Localization, Ph.D. dissertation, Robotics Institute, Univ. of Carnegie Mellon, Pittsburgh, PA, 2001.

指導教授

曾定章(Din-chang Tseng)

審核日期

2010-7-26

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