為節省人力與檢索時間，自動化物件分類與偵測逐漸成為分析監視影片不可或缺的核心技術。然而，對於一個包含大量監視器之大型監視系統，傳統的監督式物件偵測需要經過冗長的訓練過程。本篇論文提出一套非監督式物件分類與偵測演算法，可在沒有人工標記的狀況下分辨不同的前景物件。首先於目標影片擷取前景物件，並使用異質特徵來描述前景物體。由於物體出現時間長短不同，本論文也提出匹配演算法來比對出現時間長短不同的物體。經由在異質特徵空間下將物體分群後，最後以樹型分群演算法組合相似的物體。本方法以實際拍攝之道路監視影片作驗證，實驗結果顯示本方法對於前景物件分群之有效性。

Automated object detection schemes are essential in analyzing surveillance videos. However, for a large surveillance system with numerous cameras, supervised object detection methods require a lengthy training process. In this paper, we propose an unsupervised approach for identifying different foreground objects. Foreground objects are extracted from videos and represented by heterogeneous features. To assess similarities among feature vectors with unequal lengths, a sequence matching procedure is proposed. Then, different properties of foreground objects are identified by clustering feature vectors in each feature space. Finally, a tree-structured clustering algorithm is used to identify the foreground objects with similar properties. The results of real data set confirm the effectiveness of the proposed algorithm in separating different foreground objects.

[1] Y. K. Jung, K. W. Lee and Y. S. Ho, "Content-Based Event Retrieval Using Semantic Scene Interpretation for Automated Traffic Surveillance," IEEE Transactions on Intelligent Transportation Systems, vol. 2, no. 3, pp. 151-163, 2001.
[2] W. Hu, D. Xie, Z. Fu, W. Zeng and S. Maybank, "Semantic-Based Surveillance Video Retrieval," IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 1168-1181, 2007.
[3] R. Feris, B. Siddiquie, Y. Zhai, J. Petterson, L. Brown and S. Pankanti, "Attribute-Based Vehicle Search in Crowded Surveillance Videos," Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 18, 2011.
[4] R. Feris, S. Pankanti and B. Siddiquie, "Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance," Proceedings of IEEE International Conference on Multimedia and Expo, pp. 284-289, 2012.
[5] C. Piciarelli and G. L. Foresti, "Surveillance-Oriented Event Detection in Video Streams," IEEE Intelligent Systems, vol. 26, no. 3, pp. 32-41, 2011.
[6] Y. Xu, C. Wu and J. Zheng, "A Hidden Markov Model Method for Traffic Incident Detection Using Multiple Features," Proceedings of International Conference on Information Science and Technology, pp. 1183-1187, 2011.
[7] D. Gerónimo, A.M. López, A.D. Sappa and T. Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 7, pp. 1239-1258, July 2010.
[8] V. Milanés, D. F. Llorca, J. Villagrá, J. Pérez, C. Fernández, I. Parra, C. González and M. A. Sotelo, "Intelligent Automatic Overtaking System Using Vision for Vehicle Detection," Expert Systems with Applications, vol. 39, no. 3, pp. 3362-3373, 2012.
[9] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005.
[10] X. Wang, T. X. Han and S. Yan, "An HOG-LBP Human Detector with Partial Occlusion Handling," Proceedings of IEEE International Conference on Computer Vision, pp. 32-39, 2009.
[11] Q. Zhu, S. Avidan, M. Yeh and K. Cheng, "Fast Human Detection Using a Cascade of Histograms of Oriented Gradients," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1491-1498, 2006.
[12] S. Maji, A. Berg and J. Malik, "Classification Using Intersection Kernel Svms Is Efficient," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[13] P. Dolla´ r, S. Belongie and P. Perona, "The Fastest Pedestrian Detector in the West," Proceedings of British Machine Vision Conference, vol. 2, no. 3, p. 7, 2010.
[14] D.M. Gavrila, "A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1408-1421, Aug. 2007.
[15] P. Sabzmeydani and G. Mori, "Detecting Pedestrians by Learning Shapelet Features," Proceedings of IEEE International Conference on Computer Vision, pp. 1-8, 2007.
[16] P. A. Viola, M. J. Jones and D. Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance," International Journal of Computer Vision, vol. 63, no. 2, pp. 153-161, 2005.
[17] N. Dalal, B. Triggs and C. Schmid, "Human Detection Using Oriented Histograms of Flow and Appearance," Proceedings of European Conference on Computer Vision, pp. 428-441, 2006.
[18] S. Walk, N. Majer, K. Schindler and B. Schiele, "New Features and Insights for Pedestrian Detection," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1030-1037, 2010.
[19] P. V. K. Borges, "Pedestrian Detection Based on Blob Motion Statistics," IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 224-235, 2013.
[20] L. Bourdev and J. Malik, "Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1365-1372, 2009.
[21] B. Wu and R. Nevatia, "Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors," Proceedings of IEEE International Conference on Computer Vision, vol. 1, pp. 90-97, 2005.
[22] Z. Lin and L. S. Davis, "A Pose-Invariant Descriptor for Human Detection and Segmentation," Proceedings of European Conference on Computer Vision, pp. 423-436, 2008.
[23] P. Felzenszwalb, D. McAllester and D. Ramanan, "A Discriminatively Trained, Multiscale, Deformable Part Model," Proceedings of IEEE International Conference on Computer Vision, pp. 1-8, 2008.
[24] P. F. Felzenszwalb, R. B. Girshick, D. McAllester and D. Ramanan, "Object Detection with Discriminatively Trained Part Based Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627-1645, 2010.
[25] X. Ma and W. E. L. Grimson, "Edge-Based Rich Representation for Vehicle Classification," Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1185-1192, 2005.
[26] R. S. Feris, B. Siddiquie, J. Petterson, Y. Zhai, A. Datta, L. M. Brown and S. Pankanti, "Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos," IEEE Transactions on Multimedia, vol. 14, no. 1, pp. 28-42, 2012.
[27] Z.W. Kim and J. Malik, "Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking," Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 524-531, 2003.
[28] X. Liu, L. Lin, S. Yan, H. Jin and W. Tao, "Integrating Spatio-Temporal Context with Multiview Representation for Object Recognition in Visual Surveillance," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 4, pp. 393-407, 2011.
[29] W. Hu, "Learning 3D object templates by hierarchical quantization of geometry and appearance spaces," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2336-2343, 2012.
[30] Z. Zhang, T. Tan, K. Huang and Y. Wang, "Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles," IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 1-13, 2012.
[31] W. Zheng and L. Liang, "Fast Car Detection Using Image Strip Features," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2703-2710, 2009.
[32] L. W. Tsai, J. W. Hsieh and K. C. Fan, "Vehicle Detection Using Normalized Color and Edge Map," IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 850-864, 2007.
[33] X. Li, X. C. Yao, Y. L. Murphey, R. Karlsen and G. Gerhart, "A Real-Time Vehicle Detection and Tracking System in Outdoor Traffic Scenes," Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 761-764, 2004.
[34] Y. Wang, "Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field," IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 3, pp. 437-441, 2009.
[35] X. Wang, K. T. Ma, G. W. Ng and W. E. L. Grimson, "Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models," International Journal of Computer Vision, vol. 95, no. 3, pp. 287-312, 2011.
[36] B. T. Morris and M. M. Trivedi, "Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2287-2301, Nov. 2011.
[37] A. Prati, S. Calderara and R. Cucchiara, "Using Circular Statistics for Trajectory Shape Analysis," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[38] N. Anjum and A. Cavallaro, "Multifeature object trajectory clustering for video analysis," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1555-1564, 2008.
[39] J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14, pp. 281-297, 1967.
[40] L. Kaufman and P. J. Rousseeuw, "Clustering by means of Medoids," Statistical Data Analysis Based on the L_1–Norm and Related Methods, pp. 405-416, 1987.
[41] M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," Proceedings of the Second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 96, pp. 226-231, 1996.
[42] M. Ankerst, M. M. Breunig, H.-P. Kriegel and J. Sander, "Optics: Ordering Points to Identify the Clustering Structure," ACM Sigmod Record, vol. 28, no. 2, pp. 49-60, 1999.
[43] A. Y. Ng, M. I. Jordan and Y. Weiss, "On Spectral Clustering: Analysis and an algorithm," Proceedings of Advances in Neural Information Processing Systems, pp. 849-856, 2002.
[44] J. F. Brendan and D. Dueck, "Clustering by Passing Messages Between Data Points," Science, vol. 315, no. 5814, pp. 972-976, 2007.
[45] J. Gao, W. Fan, Y. Sun and J. Han, "Heterogeneous Source Consensus Learning via Decision Propagation and Negotiation," Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 339-348, 2009.
[46] X. Cai, F. Nie, H. Huang and F. Kamangar, "Heterogeneous Image Feature Integration via Multi-Modal Spectral Clustering," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1977-1984, 2011.
[47] C. R. Huang, H. C. Cheng and P. C. Chung, "Online Surveillance Video Synopsis," Proceedings of IEEE International Symposium on Circuits and Systems, pp. 1843-1846, 2012.
[48] D. Y. Chen, K. Cannons, H. R. Tyan, S. W. Shih and H. Y. M. Liao, "Spatiotemporal Motion Analysis for the Detection and Classification of Moving Targets," IEEE Transactions on Multimedia, vol. 10, no. 8, pp. 1578-1591, Dec. 2008.
[49] M. Vlachos, G. Kollios and D. Gunopulos, "Discovering Similar Multidimensional Trajectories," Proceedings of the 18th International Conference on Data Engineering, pp. 673-684, 2002.
[50] M. J. Swain and H. D. Ballard, "Color Indexing," International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[51] C. D. Manning, P. Raghavan and H. Schütze, "Introduction to Information Retrieval," Cambridge University Press, pp. 356-358, 2008.