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Issue No.06 - June (2011 vol.33)
pp: 1250-1265
Aniruddha Kembhavi , Microsoft Corporation, Redmond
David Harwood , University of Maryland, College Park
Larry S. Davis , University of Maryland, College Park
Detecting vehicles in aerial images has a wide range of applications, from urban planning to visual surveillance. We describe a vehicle detector that improves upon previous approaches by incorporating a very large and rich set of image descriptors. A new feature set called Color Probability Maps is used to capture the color statistics of vehicles and their surroundings, along with the Histograms of Oriented Gradients feature and a simple yet powerful image descriptor that captures the structural characteristics of objects named Pairs of Pixels. The combination of these features leads to an extremely high-dimensional feature set (approximately 70,000 elements). Partial Least Squares is first used to project the data onto a much lower dimensional subspace. Then, a powerful feature selection analysis is employed to improve the performance while vastly reducing the number of features that must be calculated. We compare our system to previous approaches on two challenging data sets and show superior performance.
Vehicle detection, partial least squares, feature selection.
Aniruddha Kembhavi, David Harwood, Larry S. Davis, "Vehicle Detection Using Partial Least Squares", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 6, pp. 1250-1265, June 2011, doi:10.1109/TPAMI.2010.182
[1] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[2] A. Berg and J. Malik, “Geometric Blur for Template Matching,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2001.
[3] A.-L. Boulesteix and K. Strimmer, “Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data,” Briefings in Bioinformatics, vol. 8, no. 1, pp. 32-44, 2007.
[4] L.-F. Chen, H.-Y.M. Liao, M.-T. Ko, J.-C. Lin, and G.-J. Yu, “A New Lda-Based Face Recognition System Which Can Solve the Small Sample Size Problem,” Pattern Recognition, vol. 33, pp. 1713-1726, 2000.
[5] J.-Y. Choi and Y.-K. Yang, “Vehicle Detection from Aerial Images Using Local Shape Information,” Proc. Third Pacific Rim Symp. Advances in Image and Video Technology, 2008.
[6] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2005.
[7] L. Eikvil, L. Aurdal, and H. Koren, “Classification-Based Vehicle Detection in High-Resolution Satellite Images,” ISPRS J. Photogrammetry and Remote Sensing, vol. 64, no. 1, pp. 65-72, 2009.
[8] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2008 (VOC2008) Results,” challenges/ VOC/voc2008/workshopindex.html , 2008.
[9] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A Library for Large Linear Classification,” J. Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
[10] P.H. Garthwaite, “An Interpretation of Partial Least Squares,” J. Am. Statistical Assoc., vol. 89, no. 425, pp. 122-127, 1994.
[11] P. Geladi and B. Kowalski, “Partial Least-Squares Regression: A Tutorial,” Analytica Chimica Acta, vol. 185, pp. 1-17, 1986.
[12] H. Grabner, T. Nguyen, B. Gruber, and H. Bischof, “On-Line Boosting-Based Car Detection from Aerial Images,” ISPRS J. Photogrammetry and Remote Sensing, vol. 63, no. 3, pp. 382-396, 2008.
[13] G. Heitz and D. Koller, “Learning Spatial Context: Using Stuff to Find Things,” Proc. European Conf. Computer Vision, 2008.
[14] I. Helland, “On the Structure of Partial Least Squares,” Comm. in Statistics., Simulation, and Computation, vol. 17, pp. 581-607, 1988.
[15] S. Hinz, “Detection and Counting of Cars in Aerial Images,” Proc. Int'l Conf. Image Processing, 2003.
[16] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Match for Recognizing Natural Scene Categories,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
[17] D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[18] S. Maji and A.C. Berg, “Max-Margin Additive Classifiers for Detection,” Proc. IEEE Int'l Conf. Computer Vision, 2009.
[19] S. Maji, A.C. Berg, and J. Malik, “Classification Using Intersection Kernel Support Vector Machines Is Efficient,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[20] H. Moon, R. Chellappa, and A. Rosenfeld, “Optimal Edge-Based Shape Detection,” IEEE Trans. Image Processing, vol. 11, no. 11, pp. 1209-1227, Nov. 2002.
[21] H. Moon, R. Chellappa, and A. Rosenfeld, “Performance Analysis of a Simple Vehicle Detection Algorithm,” Image and Vision Computing, vol. 20, no. 1, pp. 1-13, 2002.
[22] V.I. Morariu, B.V. Srinivasan, V.C. Raykar, R. Duraiswami, and L.S. Davis, “Automatic Online Tuning for Fast Gaussian Summation,” Proc. Advances in Neural Information Processing Systems, 2008.
[23] V.C. Raykar and R. Duraiswami, “Fast Optimal Bandwidth Selection for Kernel Density Estimation,” Proc. SIAM Int'l Conf. Data Mining, 2006.
[24] S. Reinikainen and A. Hoskuldsson, “Covproc Method: Strategy in Modeling Dynamic Systems,” J. Chemometrics, vol. 17, pp. 130-139, 2003.
[25] C. Schlosser, J. Reitberger, and S. Hinz, “Automatic Car Detection in High Resolution Urban Scenes Based on an Adaptive 3d Model,” Proc. Second GRSS/ISPRS Joint Workshop Remote Sensing and Data Fusion over Urban Areas, 2003.
[26] F. Tanner, B. Colder, C. Pullen, D. Heagy, M. Eppolito, V. Carlan, C. Oertel, and P. Sallee, “Overhead Imagery Research Data Set an Annotated Data Library and Tools to Aid in the Development of Computer Vision Algorithms,” Proc. IEEE Applied Imagery Pattern Recognition Workshop '09, 2009.
[27] R. Teofilo, J. Martins, and M. Ferreira, “Sorting Variables by Using Informative Vectors as a Strategy for Feature Selection in Multivariate Regression,” J. Chemometrics, vol. 23, pp. 32-48, 2009.
[28] P. Viola and M. Jones, “Robust Real-Time Object Detection,” Int'l J. Computer Vision, 2002.
[29] H. Wold, “Estimation of Principal Components and Related Models by Iterative Least Squares,” Multivariate Analysis, Academic Press, 1966.
[30] H. Wold, “Partial Least Squares,” Encyclopedia of Statistical Sciences, S. Kotz and N. Johnson, eds., vol. 6, pp. 581-591, Wiley, 1985.
[31] Z. Yue, D. Guarino, and R. Chellappa, “Moving Object Verification in Airborne Video Sequences,” IEEE Trans. Circuits and Systems for Video Technology, vol. 19, no. 1, pp. 77-89, Jan. 2009.
[32] J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study,” Int'l J. Computer Vision, vol. 73, no. 2, pp. 213-238, 2007.
[33] T. Zhao and R. Nevatia, “Car Detection in Low Resolution Aerial Images,” Image and Vision Computing, vol. 21, no. 8, pp. 693-703, 2003.
[34] H. Zheng, L. Pan, and L. Li, “A Morphological Neural Network Approach for Vehicle Detection from High Resolution Satellite Imagery,” Proc. Int'l Conf. Neural Information Processing, 2006.
[35] Q. Zhu, M.-C. Yeh, K.-T. Cheng, and S. Avidan, “Fast Human Detection Using a Cascade of Histograms of Oriented Gradients,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 2006.
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